Systems and methods for non-invasive blood pressure measurement

ABSTRACT

Systems and methods for non-invasive blood pressure measurement are disclosed. In some embodiments, a system comprises a wearable member configured to generate first and second signals (e.g., PPG signals), and a blood pressure calculation system. The blood pressure calculation system includes a wave selection module configured to identify subsets of waves of the signals, a feature extraction module configured to generate sets of feature vectors form the subsets of waves, and a blood pressure processing module configured to calculate an arterial blood pressure value based on the sets of feature vectors and an empirical blood pressure calculation model, the empirical blood pressure calculation model configured to receive the sets of feature vectors as input values. The blood pressure calculation system further includes a communication module configured to provide a message including or being based on the arterial blood pressure value.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/120,820 filed Feb. 25, 2015 and entitled “EchoLabs Sensors Methods and Uses—detecting biometric signals in livingtissue,” and U.S. Provisional Patent Application Ser. No. 62/262,540,filed Dec. 3, 2015 and entitled “Prediction of Blood Pressure from PPGData,” both of which are incorporated herein by reference.

BACKGROUND

Technical Field

Embodiments of the present inventions relate generally to blood metricsmeasurement. More specifically, embodiments of the present inventionsrelate to non-invasive blood pressure measurement.

Description of Related Art

Wearable activity monitoring devices are growing in popularity. Thesedevices aim to facilitate achieving a user's goal such as to loseweight, to increase physical activity, or simply to improve overallhealth. Many such devices may interface with computer software to allowvisualization of the recorded data. Nevertheless, most devices areevolved cousins of pedometers, which measure the number of steps a usertakes. Even though additional functions such as tallying the distance auser travels or calculating calorie consumptions may be added, thesedevices lack the ability to measure blood metrics.

Blood pressure is an important factor in both heart health and overallhealth. For example, elevated blood pressure may result in coronaryartery disease, heart failure and hypertrophy. Accordingly, bloodpressure monitoring has become an important component of patient health.Typically, blood pressure is monitored using a blood pressure gauge withan inflatable cuff. However, such devices are often uncomfortable andunable to provide continuous blood pressure measurement.

SUMMARY

An exemplary system comprises an energy transmitter, an energy receiver,and an analyzer. The energy transmitter may project energy at a firstwavelength and a second wavelength into tissue of a user, the firstwavelength and the second wavelength being associated with at least onenutrient of a set of nutrients in blood of the user. The energy receivermay generate a composite signal based on a fraction of the energy at thefirst wavelength and the second wavelength, the fraction of the energybeing received through the tissue of the user. The analyzer may separatethe composite signal into a first signal corresponding to the firstwavelength and a second signal corresponding to the second wavelength,and detect, in the blood of the user, a concentration of the at leastone nutrient of the set of nutrients based on the first signal and thesecond signal.

The fraction of the energy may be received by the energy receiver afterthe fraction of the energy is reflected by the tissue of the user. Thesystem may comprise a wearable member. The energy transmitter and theenergy receiver may be secured to the wearable member such that theenergy transmitter and the energy receiver are in contact or inproximity with the tissue. The analyzer may be further configured todetermine a set of blood metrics based on the first signal and thesecond signal, the concentration of at least one nutrient of the set ofnutrients being determined based on the determined set of blood metrics.The system may further comprise a user interface configured to displayat least some of the set of blood metrics. The analyzer may be furtherconfigured to compare a blood metric of the set of blood metric to athreshold and to generate an alert if the blood metric exceeds thethreshold. The set of blood metrics may comprise a blood glucoseconcentration.

The analyzer may be further configured to determine a first AC componentand a first DC component of the first signal, to determine a second ACcomponent and a second DC component of the second signal, wherein theconcentration of a nutrient of the set of nutrients is detected based onthe first AC component, the first DC component, the second AC component,and the second DC component. The system may further comprise a motiondetector configured to measure a level of motion, and the analyzer isconfigured to compare the level of motion to a threshold and to discounta measurement of the composite signal when the level of motion exceedsthe threshold. A nutrient of the set of nutrients may comprise glucose.

An exemplary method may comprise projecting energy at a first wavelengthand a second wavelength into tissue of a user, the first wavelength andthe second wavelength being associated with at least one nutrient of aset of nutrients in blood of the user, generating a composite signalbased on a fraction of the energy at the first wavelength and the secondwavelength, the fraction of the energy being received through the tissueof the user, separating the composite signal into a first signalcorresponding to the first wavelength and a second signal correspondingto the second wavelength, and detecting, in the blood of the user, aconcentration of the at least one nutrient of the set of nutrients basedon the first signal and the second signal.

Another exemplary system may comprise an energy transmitter, an energyreceiver, and an analyzer. The energy transmitter may be configured toproject energy at a first wavelength and a second wavelength into tissueof a user, the first wavelength and the second wavelength beingassociated with, in blood of the user, at least one component. The atleast one component being at least one of one of glucose, hemoglobin,triglycerides, cholesterol, bilirubin, protein, albumin, blood pH,Hematocrit, cortisol, and/or electrolytes. The energy receiver may beconfigured to generate a composite signal based on a fraction of theenergy at the first wavelength and the second wavelength, the fractionof the energy being received through the tissue of the user. Theanalyzer may be configured to separate the composite signal into a firstsignal corresponding to the first wavelength and a second signalcorresponding to the second wavelength, and to detect, in the blood ofthe user, a concentration of the at least one component based on thefirst signal and the second signal.

Other features and aspects of various embodiments will become apparentfrom the following detailed description, taken in conjunction with theaccompanying drawings, which illustrate, by way of example, the featuresof such embodiments.

Typically, blood pressure is measured non-invasively with asphygmomanometer. However, such devices are often uncomfortable and donot permit continuous blood pressure measurement. Some embodimentsdescribed herein include systems and methods for non-invasive continuousblood pressure measurement. For example, a blood metrics measurementapparatus may generate multi-channel signals (e.g., PPG signals) whichmay be provided to a blood pressure calculation system to calculatearterial blood pressure values (e.g., systolic blood value pressureand/or diastolic blood pressure value). More specifically, the bloodpressure calculation system (or the blood pressure measurementapparatus) may filter the multi-channel signals (e.g., to remove noisefrom the signals), select (or, “extract”) subsets of “high quality”waves from the multi-channel signals, select (or, “extract”) sets offeatures from each of the high quality waves, and generate sets offeature vectors based on the selected sets of features. In someembodiments, an empirical blood pressure model is used to calculatearterial blood pressure values based on the sets of feature vectors.

In various embodiments, a system comprises a wearable member and a bloodpressure calculation system. The wearable member may include an energytransmitter configured to project energy at a first wavelength andenergy at a second wavelength into tissue of a user, and an energyreceiver configured to generate a first signal based on a first receivedportion of the energy at the first wavelength and a second signal basedon a second received portion of the energy at the second wavelength, thefirst received portion of energy and the second received portion ofenergy each being received through the tissue of the user. The bloodpressure calculation system may include a wave selection moduleconfigured to identify a first subset of waves from a first set of wavesof the first signal and a second subset of waves from a second set ofwaves of the second signal, each of the first subset of wavesrepresenting a separate approximation of an average of the first set ofwaves over a predetermined amount of time and each of the second subsetof waves representing a separate approximation of an average of thesecond set of waves over the predetermined amount of time. The bloodpressure calculation system may further include a feature extractionmodule configured to generate a first set of feature vectors and asecond set of feature vectors, the first set of feature vectorsgenerated from the first subset of waves, the second set of featurevectors generated from the second subset of waves, wherein each of thefeature vectors of the first set of feature vectors and the second setof feature vectors include measurement values and metric values, themeasurement values corresponding to amplitude or location points of aparticular wave, the metric values generated from metric functions thatuse at least one of the measurement values. The blood pressurecalculation system may additionally include a blood pressure processingmodule configured to calculate an arterial blood pressure value based onthe first set of feature vectors, the second set of feature vectors, andan empirical blood pressure calculation model, the empirical bloodpressure calculation model configured to receive the first set offeature vectors and the second set of feature vectors as input values.The blood pressure calculation system may further include acommunication module configured to provide a message including or beingbased on the arterial blood pressure value.

In some embodiments, the energy transmitter includes a first lightsource and a second light source, the first light source configured toproject the energy at the first wavelength, the second light sourceconfigured to project the energy at the second wavelength. In relatedembodiments, the first light source and the second light source eachcomprise a light-emitting diode (LED).

In some embodiments, the measurement values include any of wave peaklocations or amplitudes, or wave valley locations or amplitudes.

In some embodiments, the measurement values include any of an associatedwave's first or higher order derivative peak locations or amplitudes,the associated wave's first or higher order derivative valley locationsor amplitudes, or first or higher order moments of the associated wave.

In some embodiments, the metric functions include one or more particularmetric functions that calculate a distance between two measurementvalues.

In some embodiments, the energy projected by the first light source andthe energy projected by second light source each have the samewavelength.

In some embodiments, the feature extraction module is further configuredto determine a phase shift between the first signal and the secondsignal; calculate, based on the phase shift, any of a pulse wavevelocity or a pulse transit time; and the blood pressure calculationmodule further configured to calculate the arterial blood pressure valuebased on first set of feature vectors, the second set of featurevectors, any of the pulse wave velocity or the pulse transit time, theempirical blood pressure calculation model, the empirical blood pressurecalculation model further configured to receive the first set of featurevectors, the second set of feature vectors, and any of the pulse wavevelocity or the pulse transit time as input.

In some embodiments, the first light source is further configured toproject one or more wavelengths in addition to the first wavelength, andwherein the second wavelength is the same, or substantially similar to,one of the wavelengths projected from the first light source.

In some embodiments, the first signal and the second signal eachcomprise a photoplethysmogram (PPG) signal.

In various embodiments, a method comprises projecting, at an energytransmitter, energy at a first wavelength and energy at a secondwavelength into tissue of a user; generating, at the energy transmitter,a first signal based on a first received portion of the energy at thefirst wavelength and a second signal based on a second received portionof the energy at the second wavelength, the first received portion ofenergy and the second received portion of energy each being receivedthrough the tissue of the user; identifying, at a blood pressurecalculation system, a first subset of waves from a first set of waves ofthe first signal and a second subset of waves from a second set of wavesof the second signal, each of the first subset of waves representing aseparate approximation of an average of the first set of waves over apredetermined amount of time and each of the second subset of wavesrepresenting a separate approximation of an average of the second set ofwaves over the predetermined amount of time; generating, at the bloodpressure calculation system, a first set of feature vectors and a secondset of feature vectors, the first set of feature vectors generated fromthe first subset of waves, the second set of feature vectors generatedfrom the second subset of waves, wherein each of the feature vectors ofthe first set of feature vectors and the second set of feature vectorsinclude measurement values and metric values, the measurement valuescorresponding to amplitude or location points of a particular wave, themetric values generated from metric functions that use at least one ofthe measurement values; calculating, at the blood pressure calculationsystem, an arterial blood pressure value based on the first set offeature vectors, the second set of feature vectors, and an empiricalblood pressure calculation model, the empirical blood pressurecalculation model configured to receive the first set of feature vectorsand the second set of feature vectors as input values; and providing,from the blood pressure calculation system, a message including or beingbased on the arterial blood pressure value.

In some embodiments, the energy transmitter includes a first lightsource and a second light source, the first light source configured toproject the energy at the first wavelength, the second light sourceconfigured to project the energy at the second wavelength. In relatedembodiments, the first light source and the second light source eachcomprise a light-emitting diode (LED).

In some embodiments, the measurement values include any of wave peaklocations or amplitudes, or wave valley locations or amplitudes.

In some embodiments, the measurement values include any of an associatedwave's first or higher order derivative peak locations or amplitudes,the associated wave's first or higher order derivative valley locationsor amplitudes, or first or higher order moments of the associated wave.

In some embodiments, the metric functions include one or more particularmetric functions that calculate a distance between two measurementvalues.

In some embodiments, the energy projected by the first light source andthe energy projected by second light source each have the samewavelength. In related embodiments, the method may further comprisedetermining a phase shift between the first signal and the secondsignal; calculating, based on the phase shift, any of a pulse wavevelocity or a pulse transit time, wherein the blood pressure calculationmodule is further configured to calculate the arterial blood pressurevalue based on first set of feature vectors, the second set of featurevectors, any of the pulse wave velocity or the pulse transit time, theempirical blood pressure calculation model, the empirical blood pressurecalculation model further configured to receive as the input the firstset of feature vectors, the second set of feature vectors, and any ofthe pulse wave velocity or the pulse transit time.

In some embodiments, the first light source is further configured toproject one or more wavelengths in addition to the first wavelength, andwherein the second wavelength is the same, or substantially similar to,one of the wavelengths projected from the first light source.

In some embodiments, the first signal and the second signal eachcomprise a photoplethysmogram (PPG) signal.

In various embodiments, a system comprises a communication interfaceconfigured to receive a first signal and a second signal, the firstsignal being based on a first received portion of energy having beenpreviously projected at a first wavelength into tissue of a user, thesecond signal being based on a second received portion of energy havingbeen previously projected at a second wavelength into the tissue of theuser; a wave selection module configured to identify a first subset ofwaves from the first set of waves of a first signal and a second subsetof waves from a second set of waves of the second signal, each of thefirst subset of waves representing a separate approximation of anaverage of the first set of waves over a predetermined amount of timeand each of the second subset of waves representing a separateapproximation of an average of the second set of waves over thepredetermined amount of time; a feature extraction module configured togenerate a first set of feature vectors and a second set of featurevectors, the first set of feature vectors generated from the firstsubset of waves, the second set of feature vectors generated from thesecond subset of waves, wherein each of the feature vectors of the firstset of feature vectors and the second set of feature vectors includemeasurement values and metric values, the measurement valuescorresponding to amplitude or location points of a particular wave, themetric values generated from metric functions that use at least onemeasurement value; a blood pressure processing module configured tocalculate an arterial blood pressure value based on the first set offeature vectors, the second set of feature vectors, and an empiricalblood pressure calculation model, the empirical blood pressurecalculation model configured to receive the first set of feature vectorsand the second set of feature vectors as input values; and acommunication module configured to provide a message including or beingbased on the arterial blood pressure value.

In various embodiments, a system comprises a processor; and memorystoring instructions that, when executed by the processor, cause theprocessor to: receive a first signal and a second signal, the firstsignal being based on a first received portion of energy having beenpreviously projected at a first wavelength into tissue of a user, thesecond signal being based on a second received portion of energy havingbeen previously projected at a second wavelength into the tissue of theuser; identify a first subset of waves from a first set of waves of thefirst signal and a second subset of waves from a second set of waves ofthe second signal, each of the first subset of waves representing aseparate approximation of an average of the first set of waves over apredetermined amount of time and each of the second subset of wavesrepresenting a separate approximation of an average of the second set ofwaves over the predetermined amount of time; generate a first set offeature vectors and a second set of feature vectors, the first set offeature vectors generated from the first subset of waves, the second setof feature vectors generated from the second subset of waves, whereineach of the feature vectors of the first set of feature vectors and thesecond set of feature vectors include measurement values and metricvalues, the measurement values corresponding to amplitude or locationpoints of a particular wave, the metric values generated from metricfunctions that use at least one of the measurement values; calculate anarterial blood pressure value based on the first set of feature vectors,the second set of feature vectors, and an empirical blood pressurecalculation model, the empirical blood pressure calculation modelconfigured to receive the first set of feature vectors and the secondset of feature vectors as input values; and provide a message includingor being based on the arterial blood pressure value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example environment utilizinga multispectral blood metrics measurement apparatus in accordance withvarious embodiments.

FIG. 2 is a block diagram illustrating an exemplary multispectral bloodmetrics measurement apparatus, such as the multispectral blood metricsmeasurement apparatus illustrated in FIG. 1

FIG. 3 illustrates an exemplary flow diagram of a method of measuringblood metrics in accordance with an embodiment of the presentapplication.

FIG. 4 illustrates an exemplary apparatus for measuring various bloodmetrics in accordance with an embodiment of the present application.

FIG. 5 illustrates a display of an assessment of a current health indexderived from data collected from or with a multispectral blood metricsmeasurement apparatus in various embodiments.

FIG. 6 illustrates a display of an assessment of an overall healthindex, derived from data collected from or with a multispectral bloodmetrics measurement apparatus in various embodiments.

FIG. 7 illustrates a display of an assessment of an overall healthindex, derived from data collected from or with a multispectral bloodmetrics measurement apparatus in various embodiments.

FIG. 8 is a block diagram illustrating an exemplary digital device thatcan be utilized in the implementation of various embodiments.

FIG. 9 illustrates a diagram of a system and environment fornon-invasive blood pressure measurement.

FIG. 10 depicts a block diagram of a blood metrics measurement apparatusaccording to some embodiments.

FIG. 11 shows a flowchart of an example method of operation of a bloodmetrics measurement apparatus according to some embodiments.

FIG. 12 depicts a block diagram of a user device according to someembodiments.

FIG. 13 shows a flowchart of an example method of operation of a userdevice according to some embodiments.

FIG. 14 depicts a block diagram of a blood pressure calculation systemaccording to some embodiments.

FIG. 15 shows a flowchart of an example method of operation of a bloodpressure calculation system according to some embodiments.

FIG. 16 shows a flowchart of an example method of operation of a bloodpressure calculation system according to some embodiments.

FIG. 17 depicts a block diagram of a blood metrics server according tosome embodiments.

FIGS. 18-20 show flowcharts of example methods of operation of a bloodmetrics server according to some embodiments.

FIG. 21 shows an example noisy PPG signal and an example filtered PPGsignal according to some embodiments.

FIG. 22 shows an example set of waves of a PPG signal and an examplehigh quality wave selected from the set of waves according to someembodiments.

FIG. 23 shows example feature points of a wave according to someembodiments.

FIG. 24 shows an example feature vector according to some embodiments.

FIGS. 25A-C show an example selected high quality wave, the firstderivative of the selected high quality wave, and the second derivativeof the selected high quality wave according to some embodiments.

FIG. 26 shows example tree structures of an example empirical bloodpressure calculation model according to some embodiments.

FIG. 27 shows an example bi-Gaussian mixture model for a PPG signalaccording to some embodiments.

DETAILED DESCRIPTION

Biometrics including blood metrics may be measured by minimally invasiveprocedures to address medical conditions such as diabetes or in thediagnosis and discovery of diseases. Minimal-invasive procedure baseddevices may have the advantages of reducing costs and decreasing theneed for invasive methods, thereby increasing the comfort and well-beingof users and patients. Even though these devices have revolutionizedpatient care, they have only been described in, and approved for,medical purposes. Minimal-invasive procedure based devices are usuallyout of reach for the general public because they are designed formedical uses rather than non-medical purposes such as fitness,well-being, and quality of life.

Personal devices such as sphygmomanometers or pulse oximeters measureblood pressure or oxygen levels, respectively, on a per-request basis.They usually cannot measure blood metrics real time or periodically.Real-time blood metrics data (e.g., high resolution measurements, ormeasurements over long periods of time) may allow these devices tofacilitate users monitoring and controlling their energy levels and/ormetabolism. Nutritionists, people suffering from obesity, peopledesiring to eat healthier, fitness enthusiasts, semi-professionalathletes, people likely to have hypoglycemia, or the vast majority ofthe general population can benefit from these devices.

In various embodiments, a multispectral blood metric measurementapparatus monitors blood metrics, fitness, and/or metabolism levels ofvarious users in a non-invasive manner. The multispectral blood metricmeasurement apparatus may be, for example, wearable technology. Themultispectral blood metric measurement apparatus may measure any numberof blood metrics. Blood metrics may include, for example, variousnutrient blood concentrations. Blood metrics may be, for example,monitored, stored, tracked, and/or analyzed.

FIG. 1 is a block diagram illustrating an example environment 100utilizing a multispectral blood metrics measurement apparatus 102 inaccordance with various embodiments. As shown in FIG. 1, the exampleenvironment 100 may comprise a multispectral blood metrics measurementapparatus 102, one or more user systems 104, an optional analysis system108, and a computer network 106 communicatively coupling together eachof the multispectral blood metrics measurement apparatus 102, one ormore user devices 110, 112, and 114 (depicted as user system 104),and/or the analysis system 108. As shown, a user system 104 may includea smartphone 110 (e.g., iPhone®), a computer 112 (e.g., a personalcomputer), and/or a tablet 114 (e.g., iPad®), through the computernetwork 106 (e.g., a Bluetooth® 4.0 personal area network), can eitherinteract directly or indirectly with the blood metrics measurementapparatus 102.

The multispectral blood metrics measurement apparatus 102 may measurehealth or metabolism predictors non-invasively. The multispectral bloodmetrics measurement apparatus 102 may measure blood metrics such asconcentrations of various nutrients over time, deliver energy intotissues of various body parts of a user, track a user's behaviorpattern, detect motion, communicate various blood metric measurements,and/or receive a user's instructions. For instance, through the computernetwork 106, the multispectral blood metrics measurement apparatus 102may transmit one or more blood metric measurements to, or receiveinstructions from, the user system 104 or the multispectral bloodmeasurement system 108 such as which health or metabolism predictor tomeasure.

In some embodiments, the multispectral blood metric 102 measurementapparatus may project energy into tissue of a user and detect energyreflected from and/or transmitted through tissue of the user (e.g., thewearer of the multispectral blood metric measurement apparatus 102). Theprojected energy may be at multiple wavelengths that are associated withthe blood metrics of interest to a user. The detected energy may be afraction of the energy that is projected into the tissue. Energy atdifferent wavelengths may be absorbed at a different rate that isrelated to a user's body state. The user's body state (e.g., heart rate,blood pressure, nutrient level, or the like) determines the amount ofabsorbed energy. Accordingly, energy at different wavelengths may beabsorbed at different levels by a user's body. The fraction of energyreceived (e.g., that is reflected by the tissue or transmitted throughthe tissue) may be used to generate signals (e.g., composite signals) atdifferent levels. These signals may provide information of the user'sbody state. This information may be obtained by analyzing waveforms ofthe signal in the time domain and/or the frequency domain.

In various embodiments, the multispectral blood metric measurementapparatus 102 may measure many metrics, including, but not limited to,skin conductivity, pulse, oxygen blood levels, blood pressure, bloodglucose level, glycemic index, insulin index, Vvo2max, fat bodycomposition, protein body composition, blood nutrient level (e.g.,iron), body temperature, blood sodium levels, and/or naturally-producedchemical compound level (e.g., lactic acid). Nutrients may be determinedbased on the blood metrics to be measured. Nutrients may be measured mayinclude, but are not limited to, glucose, hemoglobin, triglycerides,cholesterol, bilirubin, protein, albumin (i.e., egg white), and/orelectrolytes (e.g., sodium, potassium, chloride, bicarbonate, etc.)

Those skilled in the art will appreciate that the user's body state maychange dynamically and energy at a wavelength may be absorbeddifferently by a user over the time. By monitoring and tracking detectedenergy from the user's body, a user's health or condition may be moretracked. Systems and methods described herein may monitor and storeblood metrics including concentrations of various nutrients. A user'shistory health records may be generated by using blood metrics measuredat different times. In some embodiments, blood metrics measured a giventime point may be compared to the history health records to detect anyabnormal health conditions. The multispectral blood metric measurementapparatus may comprise a user interface where a user may input bloodmetrics of interest, be presented with various health reports, and/or bealerted with abnormal health conditions.

A user may comfortably wear a multispectral blood metric measurementapparatus 102 over time. The multispectral blood metric measurementapparatus 102 may comprise lightweight components. The multispectralblood metric measurement apparatus 102 may be made of hypoallergenicmaterials. The multispectral blood metric measurement apparatus 102 maybe flexibly built so that it could fit various body parts (e.g., wrist,earlobe, ankle, or chest) of a user.

In accordance with some embodiments, the computer network 106 may beimplemented or facilitated using one or more local or wide-areacommunications networks, such as the Internet, WiFi networks, WiMaxnetworks, private networks, public networks, personal area networks(“PAN”), and the like. In some embodiments, the computer network 106 maybe a wired network, such as a twisted pair wire system, a coaxial cablesystem, a fiber optic cable system, an Ethernet cable system, a wiredPAN constructed with USB and/or FireWire connections, or other similarcommunication network. Alternatively, the computer network 106 may be awireless network, such as a wireless personal area network, a wirelesslocal area network, a cellular network, or other similar communicationnetwork. Depending on the embodiment, some or all of the communicationconnections with the computer network 106 may utilize encryption (e.g.,Secure Sockets Layer [SSL]) to secure information being transferredbetween the various entities shown in the example environment 100.

Although FIG. 1 depicts a computer network 106 supporting communicationbetween different digital devices, those skilled in the art willappreciate that the multispectral blood metrics measurement apparatusmay be directly coupled (e.g., over a cable) with any or all of the userdevices 110, 112, and 114.

The user devices 110-114 may include any digital device capable ofexecuting an application related to measuring blood metrics, presentingan application user interface through a display and/or communicatingwith various entities in the example environment 100 through thecomputer network 106. For instance, through the computer network 106,the user device 110 may receive one or more blood metric measurementsfrom the multispectral blood metrics measurement apparatus 102, trackand store the blood metric measurements, analyze the blood metricmeasurements, and/or provide recommendations based on the blood metricmeasurements. An application user interface may facilitate interactionbetween a user of the user system 104 and an application running on theuser system 104.

In various embodiments, any of user devices 110-114 may perform analysisof the measurements from the multispectral blood metrics measurementapparatus 102, display results, provide reports, display progress,display historic readings, track measurements, track analysis, providealerts, and/or the like.

The analysis system 108 may be any form of digital device capable ofexecuting an analysis application for analyzing and/or measuring bloodmetrics. In some embodiments, the analysis system 108 may generatereports or generate alerts based on analysis or measurement of bloodmetrics. For instance, through the computer network 106, the analysissystem 108 may receive one or more blood metric measurements from themultispectral blood metrics measurement apparatus 102, track and storeblood metric measurements, analyze blood metric measurements, and/orprovide recommendations based on the analysis. An applicationprogramming interface may facilitate interaction between a user, theuser devices 110-114, and/or the multispectral blood metrics measurementapparatus 110 with the analysis system 108.

Computing devices (e.g., digital devices) may include a mobile phone, atablet computing device, a laptop, a desktop computer, personal digitalassistant, a portable gaming unit, a wired gaming unit, a thin client, aset-top box, a portable multi-media player, or any other type of networkaccessible user device known to those of skill in the art. Further, theanalysis system 108 may comprise of one or more servers, which may beoperating on or implemented using one or more cloud-based services(e.g., System-as-a-Service [SaaS], Platform-as-a-Service [PaaS], orInfrastructure-as-a-Service [IaaS]).

It will be understood that for some embodiments, the components or thearrangement of components may differ from what is depicted in FIG. 1.

Each of the multispectral blood metrics measurement apparatus 102, oneor more user devices 110, 112, and 114, and the analysis system 108 maybe implemented using one or more digital devices. An exemplary digitaldevice is described regarding FIG. 8.

FIG. 2 is a block diagram illustrating an exemplary multispectral bloodmetrics measurement apparatus 200, such as the multispectral bloodmetrics measurement apparatus 102 illustrated in FIG. 1. Themultispectral blood metrics measurement apparatus 200 comprises ananalyzer 202, an energy transmitter 204, and an energy receiver 206.Various embodiments may comprise a wearable member. The wearable membermay include, for example, a bracelet, glasses, necklace, ring, anklet,belt, broach, jewelry, clothing, or any other member of combination ofmembers that allow the multispectral blood metrics measurement apparatus200 to be close to or touch a body of the wearer.

The energy transmitter 204 and the energy receiver 206 may be secured tothe wearable member such that the energy transmitter and the energyreceiver may make contact or be in proximity with tissues (e.g., skin)of a user. The analyzer 202 may be coupled to the energy transmitter 204and the energy receiver 206. In further embodiments, the multispectralblood metrics measurement apparatus 200 may comprise a communicationmodule (not shown). The communication module may be coupled to theanalyzer 202. The blood metrics measurement apparatus 200 may furthercomprise a driver (not shown) and a power source (not shown). The drivermay be coupled to the energy transmitter 204 and the analyzer 202. Theanalyzer 202 may be coupled to the energy transmitter 204 via thedriver. The power source may be coupled to the energy transmitter 204via the driver. The blood metrics measurement apparatus 200 may furthercomprise an Analog-to-Digital Converter (“ADC”) (not shown). The ADC maybe coupled to the energy receiver 206 and the analyzer 202. In someembodiments, the blood metrics measurement apparatus 200 may comprise amotion sensor (e.g., an accelerometer, a global positioning system) (notshown). The motion sensor may be coupled to the analyzer 202.

In various embodiments, the energy transmitter 204 emits energyincluding, but not limited to, light, into the body of the user. Theenergy produced by the energy transmitter may be in the direction ofentering tissues. For example, the energy produced by the energytransmitter 204 is in a direction 251 entering the tissue 210. In someembodiments, the energy transmitter 204 emits energy or light atdifferent wavelengths. The energy transmitter 204 may comprise anynumber of light emission diodes (“LEDs”). In some embodiments, theenergy transmitter 204 comprises at least two LEDs. Each LED may beconfigured to emit energy at one or more wavelengths. In anotherexample, each LED may emit light with a peak wavelength centered arounda wavelength. In one example, the energy transmitter 204 may emit lightwith a peak wavelength centered around 500 nm to 1800 nm.

Each wavelength may correspond to one or more blood metrics of interestand/or one or more nutrients. Those skilled in the art will appreciatethat different components of the blood and/or different nutrients mayabsorb energy at different wavelengths. In various embodiments, acontroller, driver, analyzer 202, or the like may receive a blood metricor nutrient of interest (e.g., from a user of the multispectral bloodmetrics measurement apparatus 200 and/or a user device not shown). Thecontroller, driver, analyzer 202 or the like may associate the bloodmetric and/or nutrient of interest with one or more wavelengths andconfigure one or more of the LEDs to emit energy of at least one of theone or more wavelengths. For example, the analyzer 202 may command thedriver to deliver electric power to one LED that is configured to emitlight at the desired wavelength.

The energy receiver 206 may detect energy associated with the energyprovided by the LEDs from tissues (e.g., skin) of the user. In thisexample, received and/or detected energy is in the direction 252 thatleaves from the tissue 210. In various embodiments, the energy receiver206 may detect energy from the body of the user that is a fraction ofthe energy produced by the energy transmitter 204.

The energy transmitter 204 and the energy receiver 206 may be configuredsuch that the energy receiver 206 detects reflected energy from tissuesof the user of the multispectral blood metrics measurement apparatus200. For example, the energy transmitter 204 and the energy receiver 206may be configured to be disposed on one surface or side of a user'stissue. The energy transmitter 204 and the energy receiver 206 may beconfigured such that the energy receiver 206 detects energy from theenergy transmitter 204 that passes through or reflects from the user'stissues. In some embodiments, the energy transmitter 204 and the energyreceiver 206 may be configured to be disposed on different (e.g.,opposite) surfaces or sides of a users' tissue.

Energy detected from tissues of a user may be detected by the energyreceiver 206. The energy receiver 206 may be configured to generate asignal in response to the detected energy. In some embodiments, theenergy receiver 206 may be triggered by the energy received to generatean output which may be dependent or partially dependent upon the amountof energy received. The energy receiver 206 may be configured togenerate a signal (e.g., an electric current, or an electric voltage) inresponse to the energy received from the tissues.

The signal generated by the energy receiver 206 may be associated withone or more blood metrics and/or nutrients of interest. Energy atdifferent wavelengths may be absorbed at a different rate that isrelated to a user's body state. The user's body state (e.g., heart rate,blood pressure, nutrient level, or the like) may determine the amount ofenergy absorbed by the body. Accordingly, energy from the user's body atdifferent wavelengths may be detected at different levels therebycausing different responses of the energy receiver 206. The energyreceiver 206 may, for example, output signals based on the level of theenergy received.

The energy receiver 206 may provide information associated with theuser's body state. Blood metric information may be determined (e.g., bythe analyzer 202) from the output signal of the energy receiver 206.

The energy receiver 206 may comprise a set of photodetectors (e.g., aphoto diode, or a photo transistor) which are configured to output asignal dependent upon photons or the like from the energy transmitter204 that passed through tissues of the user.

In various embodiments, the output signal of the energy receiver 206 isa composite of multiple signals. Each signal of the composite may beassociated with energy at a wavelength which may be a portion (orfraction) of the total energy emitted by the energy transmitter 204.

The energy transmitter 204 may be configured to generate energy at a setof wavelengths. In some embodiments, the energy transmitter 204 isconfigured to generate energy such that energy at different wavelengthsis generated sequentially and/or periodically. The energy transmitter204 may be configured to generate energy at each particular wavelengthuntil energy at all wavelengths of the set is generated. The period oftime for the energy transmitter 204 to generate energy at allwavelengths is a generation period. Subsequent to completion of thegeneration period, the energy transmitter 204 may start a new generationperiod thereby allowing multiple measurements.

FIG. 3 illustrates an exemplary flow diagram of a method 300 ofmeasuring blood metrics in accordance with an embodiment of the presentapplication. At step 302, energy transmitter 204 generates and deliversenergy at different wavelengths into tissues (e.g., skin) of a user.Different wavelengths may be associated with any number of nutrients,which may be associated with the blood metrics to be measured.

In some embodiments, a user may define various blood metrics and/ornutrients to be measured. Referring back to FIG. 1, a list of bloodmetrics and/or nutrients may be selected from a user interface (e.g.,displayed on an interface of the multispectral blood metrics measurementapparatus 102, on a user device 110-114, or through the analysis system108). The user may select one or more blood metrics and/or nutrients tobe measured.

In some embodiments, a user may define a set of blood metrics to bemeasured on the user system 104; the multispectral blood metricsmeasurement apparatus 102 may provide the blood metrics to be measuredto the user system 104. For example, on any device of the user system104, a user may define one or more blood metrics by selecting one ormore blood metrics from a list of blood metrics provided, for example,via the user interface.

As discussed herein, the multispectral blood metrics measurementapparatus 200 may measure, but is not limited to, skin conductivity,pulse, oxygen blood levels, blood pressure, blood glucose level,glycemic index, insulin index, Vvo2max, fat body composition, proteinbody composition, blood nutrient level (e.g., iron), body temperature,blood sodium levels, or naturally-produced chemical compound level(e.g., lactic acid). Nutrients may be determined based on the bloodmetrics to be measured. The multispectral blood metrics measurementapparatus 200 may measure nutrients, but is not limited to, glucose,hemoglobin, triglycerides, cholesterol, bilirubin, protein, albumin(i.e., egg white), or electrolytes (e.g., sodium, potassium, chloride,bicarbonate, or the like). The multispectral blood metrics measurementapparatus 200 may also measure oxygen, cortisol, and Hematocrit, forexample (e.g., blood components).

In various embodiments, one or more wavelengths may be associated with anutrient or a combination of blood components or molecules. In someembodiments, a number of wavelengths generated by the energy transmitter204 are the number of blood components or molecules to be measured plusone. For example, when a total number of five (5) blood componentsand/or molecules are to be measured, a total number of six (6)wavelengths may be determined based on the blood components and/ormolecules to be measured. Similarly, those skilled in the art willappreciate that one or more wavelengths may be associated with anutrient or a combination of nutrients. In some embodiments, a number ofwavelengths generated by the energy transmitter 204 are the number ofnutrients to be measured plus one. For example, when a total number ofthree (3) nutrients are to be measured, a total number of four (4)wavelengths may be determined based on the nutrients to be measured.

In some embodiments, the multispectral blood metrics measurementapparatus 200, user devices 110-114, and/or analysis system 108 maycomprise a reference table of blood components, molecules, and/ornutrients and wavelengths corresponding to the blood components,molecules, and/or nutrients. A wavelength may be unique to or moregenerally associated with a nutrient. A reference wavelength may beunique to or more generally associated with a combination of nutrientsto be measured. As such, wavelength(s) may be determined by looking upeach blood components, molecules, and/or nutrients that is to bemeasured. Energy at the determined wavelengths may be transmitted by theenergy transmitter 204 into the body.

In various embodiments, in a predetermined time duration, energy at alldesired wavelengths may be generated. For each wavelength, thecorresponding energy may be generated for a time period equal to apredetermined time duration divided by the number of wavelengths. Forexample, four (4) wavelengths may be determined and the predeterminedtime duration is two (2) seconds. Accordingly, energy for eachwavelength may be generated for a duration of half (0.5) second.

At step 304, the energy receiver 206 detects a fraction of the energytransmitted into the user's tissue by the energy transmitter 204. Theenergy receiver 206 may generate a signal based on the fraction ofenergy detected (e.g., based on the amount of the energy detected). Inone example, energy detected at step 304 may be a fraction of the energygenerated at step 302 reflected by the tissue. Energy detected at step302 may be a fraction of the energy generated at step 302 that passesthrough the tissue (e.g., other undetected energy may be absorbed bytissue and/or otherwise blocked). The output signal of the energyreceiver 206 may be an electric current or an electric voltage, of whichthe amplitude may be related to the amount of the energy detected. Invarious embodiments, steps 302 and 304 are performed simultaneously.That is, energy generation and detection may be performed approximatelysimultaneously.

In various embodiments, the output signal generated by the energyreceiver 206 is a composite signal of multiple signals, each of whichcorresponds to one or more wavelengths. The output signal produced atstep 306 may be divided into individual signals, each of which is may beassociated with one or more wavelengths.

In various embodiments, analysis of the signals from the energy receiver206 may identify abnormal measurements. For example, each of themeasurement may be compared to a predetermined value. If the differencebetween the measurement and the predetermined value is above (or below)a threshold, then the measurement may be determined to be abnormal. Anabnormal value may trigger additional analysis or an alert. In someembodiments, an abnormal value is ignored (e.g., as possibly effected bynoise caused by movement of the energy transmitter 204 and/or the energyreceiver 206). In various embodiments, the abnormal value may bediscounted (e.g., the weight of the value reduced). The degree ofdiscount may be based, for example, on information from an accelerometer(e.g., a large acceleration may indicate that the abnormal value shouldbe significantly discounted) and/or based on historical values. Thoseskilled in the art will appreciate that the degree of discount may bebased on any number of factors.

In some embodiments, measurements may be averaged over a period of time.A Kalman filer (e.g., a nonlinear, unscented Kalman filter) may beapplied to any number of measurements or averaged measurements. A motionmeasurement (e.g., a measurement by an accelerometer) may be considered.Upon determining a measurement is abnormal, the motion measurement forthat time point may be inspected. A large measurement may indicate largevibrations or accelerations that corroborate that the measurement may beabnormal. Measurements collected in such situations are likely to havesignificant electrical noises.

At step 308, the analyzer 202 may analyze signals from the energyreceiver 206 analyzed in the frequency domain to determine bloodmetrics. Concentration of a nutrient in the blood may subsequently bedetermined. In some embodiments, signals may be provided to a bandpassfilter that separates AC components from DC components. An AC componentmay represent signal variation at the cardiac frequency and a DCcomponent may represent the average overall transmitted light intensity.In some embodiments, a heart rate and/or oxygen saturation, SpO₂ may bedetermined. The heart rate may be determined, for example, by averagingthe maximum frequency to determine the rate of cardiac beats in apredetermined amount of time. The oxygen saturation SpO₂ may bedetermined according to Equation (1):S_(p)O₂=110−25×R  (1),where R is the ratio of a red and infrared normalized transmitted lightintensity. R may be determined according to Equation (2):

$\begin{matrix}{{R = \frac{{AC}_{R}/{DC}_{R}}{{AC}_{IR}/{DC}_{IR}}},} & {(2),}\end{matrix}$where the AC_(R) is the AC component of the detected energycorresponding to a wavelength (e.g., red light), DC_(R) is the DCcomponent of the detected energy corresponding to the wavelength (e.g.,red light), AC_(IR) is the AC component of the detected energycorresponding to a different wavelength (e.g., infrared light), andDC_(IR) is the DC component of the detected energy corresponding to thedifferent wavelength (e.g., infrared light). In some embodiments, the ACcomponent may be selected as the highest spectral line in the cardiacfrequency band. Waveform analysis may be performed to determine the R-Rinterval defined by two successive AC components, an elapsed intervaland the perturbation, if there is any.Those skilled in the art will appreciate that analysis may be performedby the analyzer 202 and/or any other digital device (e.g., any of usersdevices 110-114 or analysis system 108).

At step 308, state space estimation and progression may be performed todetermine blood metrics. A system may be modeled according to Equation(3):x(n+1)=f[x(n)]+u(n)y(n)=h[x(n)]+v(n)  (3),where x(n) represents the state of the system, u(n) is process noise,y(n) is the vector of the observed signals, and v(n) is the measurementnoise.

Table 1 lists one or more parameters for x(n) as well as their initialvalue in some embodiments:

TABLE 1 Parameter Symbol Initial Value Cardiac frequency ƒ_(HR) 1 HzCardiac phase θ_(HR) 0 Cardiac harmonic I_(Harmonic) ^(HR) 0 amplitudeCardiac Pulse P_(HR) 1 Pressure Point Blood Pressure P_(Point) 1Respiratory ƒ_(Resp) 0.3 Hz frequency Respiratory phase θ_(Resp) 0Wavelength i = 1 . . . N I_(λ) _(i) ^(AC) 0.5 max_value AC peakamplitude Wavelength i = 1 . . . N pos_(λ) _(i) ^(AC) Corresponding FFTAC peak location bin to 1 Hz Wavelength i = 1 . . . N I_(λ) _(i) ^(DC)0.5 max_value DC Wavelength i = 1 . . . N I_(λ) _(i) ^(p2p) 1 ADC readp2p amplitude Wavelength i = 1 . . . N τ_(λ) _(i) ^(rise) 0.1 sec risetime Wavelength i = 1 . . . N c_(λ) _(i) 1 Significance coefficientWavelength i = 1 . . . N T_(λ) _(i) ^(HRV)   1 sec HRV Best Ratio pHBR_(pH) 2 Best Ratio pCO2 BR_(pCO2) 3 Best Ratio pHCO3− B R_(pHCO3) ⁻ 4Acceleration I_(move) 0 magnitude GPS velocity |v|_(GPS) 0 GPS altitude|alt|_(GPS) 0 GPS acceleration |a|_(GPS) 0 GPS incline |incline|_(GPS) 0Restfulness Rest 0 Hydration Hyd 0 Systolic Blood SBP 120 mmHg PressureDiastolic Blood DBP  80 mmHg Pressure End tidal CO2 ETCO2  40 mmHg BloodCarbon SpCO 0% Monoxide

Table 2 lists one or more parameters for y(n) as well as their initialvalue in some embodiments:

TABLE 2 Parameter Symbol Initial Blood pH pH 7.35 Blood PCO2 pCO₂ 24mmol Blood PO2 pO₂ 24 mmol Blood PHCO3− pHCO₃ ⁻ 24 mmol Blood GlucosepC₆H₁₂O₆  3 mmol Cardiac Frequency ƒ_(HR) 1 Point Blood PressureP_(Point) 1 Respiratory ƒ_(Resp) 0.3 Hz Frequency GPS velocity |v|_(GPS)0 GPS altitude |alt|_(GPS) 0 GPS acceleration |a|_(GPS) 0 GPS incline|incline|_(GPS) 0

Table 3 lists the state space model F(X(n)) between the parameterslisted in Table 1 and Table 2 in some embodiments, where the energywavelengths comprise 880 nm, 631 nm, 1450 nm, and 1550 nm:

TABLE 3 Name Symbol Equation Cardiac frequency f_(HR)${bin\_ to}{\_ freq}\left( \frac{\sum{c_{\lambda_{i}}{pos}_{\lambda_{i}}^{AC}}}{\sum c_{\lambda_{i}}} \right)$Cardiac θ_(HR) θ_(HR)(n − 1) + f_(s) ⁻¹ * ω*, where ω* ε [ω_min, ω_max]phase Cardiac harmonic amplitude I_(Harmonic) ^(HR)$\frac{\sum{c_{\lambda_{i}}I_{\lambda_{i}}^{p2p}}}{\sum c_{\lambda_{i}}}$Cardiac Pulse Pressure P_(HR)$\left( \frac{\sum{c_{\lambda_{i}}\tau_{\lambda_{i}}^{rise}}}{\sum c_{\lambda_{i}}} \right)\bigwedge{- 1}$Point Blood P_(Point) τ_(λ) ₁ ^(rise) ⁻¹ Pressure Respiratory f_(Resp)3) Respiratory and Heart Rate State Models: The fluctuations frequencyin the respiratory rate ω_(r)(n) and fluctuations in the heart rateω_(ca)(n) that are not due to RSA are both modeled as a first-orderautoregressivc process with a mean and mild nonline- arity that limitthe frequencies to know physiologic ranges ω_(r)(n + 1) = ω _(r) + α_(r){s_(r) [ω_(r)(n)] − ω _(r)} + u_(ω) _(r) (n) (15) ω_(ca)(n + 1) = ω_(c) + α_(c) {s_(c) [ω_(ca)(n)] − ω _(c)} + u_(ω) _(ca) (n) (16) where ω_(r) and ωc are the a priori estimates of the expected respiratory andcardiac frequencies, respectively; α_(r) and α_(c) con- trol thebandwidth of the frequency fluctuations; and u_(ω) _(r) (n) and u_(ω)_(ca) (n) are white noise processes that model the random vari- ation inthe respiratory and cardiac frequencies, respectively. The instantaneousrespiratory and heart rates in units of Hz are then${f_{r}(n)} = {\frac{1}{2{\pi T}_{s}}{s_{r}\left\lbrack {\omega_{r}(n)} \right\rbrack}}$(17)${f_{c}(n)} = {\frac{1}{2{\pi T}_{s}}{{s_{c}\left\lbrack {\omega_{c}(n)} \right\rbrack}.}}$(18) Respiratory θ_(Resp) θ_(Resp)(n − 1) + f_(s) ⁻¹ * ω*, where ω* ε[ω_min, ω_max] phase λ = 880 nm I_(λ) _(i) ^(AC) From FFT AC peak λ =880 nm pos_(λ) _(i) ^(AC) From FFT DC λ = 880 nm I_(λ) _(i) ^(DC) FromWaveform analysis p2p amplitude λ = 880 nm I_(λ) _(i) ^(p2p) FromWaveform analysis rise time λ = 880 nm τ_(λ) _(i) ^(rise) From Waveformanalysis signal trend λ = 880 nm c_(λ) _(i) From Waveform analysisSignificance coefficient λ = 880 nm T_(λ) _(i) ^(HRV) From Waveformanalysis HRV λ = 631 nm I_(λ) _(i) ^(AC) From Fast FourierTransformation (“FFT”) AC peak λ = 631 nm pos_(λ) _(i) ^(AC) From FFT DCλ = 631 nm I_(λ) _(i) ^(DC) From Waveform analysis p2p amplitude λ = 631nm I_(λ) _(i) ^(p2p) From Waveform analysis rise time λ = 631 nm τ_(λ)_(i) ^(rise) From Waveform analysis signal trend λ = 631 nm c_(λ) _(i)From Waveform analysis Significance coefficient λ = 631 nm T_(λ) _(i)^(HRV) From Waveform analysis HRV λ = 1450 nm I_(λ) _(i) ^(AC) From FFTAC peak λ = 1450 nm pos_(λ) _(i) ^(AC) From FFT DC λ = 1450 nm I_(λ)_(i) ^(DC) From Waveform analysis p2p amplitude λ = 1450 nm I_(λ) _(i)^(p2p) From Waveform analysis rise time λ = 1450 nm τ_(λ) _(i) ^(rise)From Waveform analysis signal trend λ = 1450 nm c_(λ) _(i) From Waveformanalysis Significance coefficient λ = 1450 nm T_(λ) _(i) ^(HRV) FromWaveform analysis HRV λ = 1550 nm I_(λ) _(i) ^(AC) From FFT AC peak λ =1550 nm pos_(λ) _(i) ^(AC) From FFT DC λ = 1550 nm I_(λ) _(i) ^(DC) FromWaveform analysis p2p amplitude λ = 1550 nm I_(λ) _(i) ^(p2p) FromWaveform analysis rise time λ = 1550 nm τ_(λ) _(i) ^(rise) From Waveformanalysis signal trend λ = 1550 nm c_(λ) _(i) From Waveform analysisSignificance coefficient λ = 1550 nm T_(λ) _(i) ^(HRV) From Waveformanalysis HRV Best Ratio B R_(pH) Device Calibration pH Best Ratio BR_(pCO2) Device Calibration pCO2 Best Ratio B R_(pHCO3)− DeviceCalibration pHCO3− Acceleration I_(move) From Accelerometer magnitudeGPS velocity |v|_(GPS) From GPS GPS altitude |alt|_(GPS) From GPS GPS|a|_(GPS) From GPS acceleration GPS incline |incline|_(GPS) From GPS

Table 4 lists Y(n)=H(x(n)):

TABLE 4 Name Symbol Equation Blood pH pH$6.1 + {\log\left( \frac{{pHCO}_{3}^{-}}{0.03{pCO}_{2}} \right)}$ BloodPCO2 pCO₂$\frac{\varepsilon_{Hb}^{{CO}_{2}} - {\varepsilon_{Hb}^{Hb}*I_{\lambda_{{CO}_{2}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{{CO}_{2}}}^{D\; C}} \right)}}}{\varepsilon_{Hb}^{{CO}_{2}} - \varepsilon_{{CO}_{2}}^{{CO}_{2}} + {\left( {\varepsilon_{{CO}_{2}}^{Hb} - \varepsilon_{Hb}^{Hb}} \right)*I_{\lambda_{{CO}_{2}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{{CO}_{2}}}^{D\; C}} \right)}}}$Blood PO2 pO₂$\frac{\varepsilon_{Hb}^{O_{2}} - {\varepsilon_{Hb}^{Hb}*I_{\lambda_{O_{2}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{O_{2}}}^{D\; C}} \right)}}}{\varepsilon_{Hb}^{O_{2}} - \varepsilon_{O_{2}}^{O_{2}} + {\left( {\varepsilon_{O_{2}}^{Hb} - \varepsilon_{Hb}^{Hb}} \right)*I_{\lambda_{O_{2}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{O_{2}}}^{D\; C}} \right)}}}$Blood PHCO3− pHCO₃ ⁻$\frac{\varepsilon_{Hb}^{{HCO}_{3}^{-}} - {\varepsilon_{Hb}^{Hb}*I_{\lambda_{{HCO}_{3}^{-}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{{HCO}_{3}^{-}}}^{D\; C}} \right)}}}{\varepsilon_{Hb}^{{HCO}_{3}^{-}} - \varepsilon_{{HCO}_{3}^{-}}^{{HCO}_{3}^{-}} + {\left( {\varepsilon_{{HCO}_{3}^{-}}^{Hb} - \varepsilon_{Hb}^{Hb}} \right)*I_{\lambda_{{HCO}_{3}^{-}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{{HCO}_{3}^{-}}}^{D\; C}} \right)}}}$Blood Glucose pC₆H₁₂O₆ As above Cardiac f_(HR) As in f(x(n)) FrequencyPoint Blood P_(Point) As in f(x(n)) Pressure Respiratory f_(Resp) As inf(x(n)) Frequency GPS velocity |v|_(GPS) As in f(x(n)) GPS altitude|alt|_(GPS) As in f(x(n)) GPS |a|_(GPS) As in f(x(n)) acceleration GPSincline |incline|_(GPS) As in f(x(n))

As illustrated in Tables 3 and 4, by generating energy at differentwavelengths, one or more blood metrics may be determined from thedetected energy. For example, cardiac frequency, cardiac phase, cardiacharmonic amplitude, cardiac pulse pressure, point blood pressure,respiratory frequency, respiratory phase, blood pH, blood pCO₂, bloodpHCO₃₋, or blood glucose, may be determined.

FIG. 4 illustrates an exemplary apparatus 400 for measuring variousblood metrics in accordance with an embodiment of the presentapplication. The apparatus 400 comprises a central unit 402, a sensorarray 404, and a coupling means 408. The central unit 402 may be awearable member made of elastic and/or flexible hypoallergenic wearablematerial.

In the illustrated example, the sensor array 404 is coupled to thecentral unit 402. The sensor array 404 may comprise any number of energytransmitters and/or energy receivers. The sensor array 404 may bedetached from the central unit 402. In some embodiments, the sensorarray 404 may be mechanically and electrically coupled to the centralunit 402. The sensor array 404 comprises various illumination (e.g.,near infra-red, infra-red, or short infra-red) and sensing array. Thesensor array 404 may further comprise conductivity and/or capacitysensors. Different sensor array 404 may be provided to measure differentblood metrics.

The central unit 402 may comprise an analyzer. In some embodiments, thecentral unit comprises an analyzer, one or more energy transmitter(s),and one or more energy receiver(s). The central unit 402 may furthercomprise a communication module and/or a battery compartment. Thecoupling means 408 are mounting screw holes in FIG. 4, however, thoseskilled in the art will appreciate that coupling means may be optional.Further, coupling means 408 may include any kind of means including aclip, hook, switch, expanding fabric, adhesive, or the like. One ofordinary skill in the art would understand that other mounting means maybe used.

The apparatus 400 further comprises a micro-USB port 406 to allow forcommunication with a digital device and a screen 410. Various userinterfaces (e.g., lights, a display, touchscreen, or the like) may bedisplayed on the screen 410.

FIGS. 5-7 are screenshots illustrating an example of presenting healthanalysis over a user interface in accordance with various embodiments.Various embodiments may store blood metrics and/or nutrientmeasurements. FIG. 5 illustrates a display 500 of an assessment of acurrent health index derived from data collected from or with amultispectral blood metrics measurement apparatus in variousembodiments. The display may appear on the user's smartphone, forexample. In various embodiments, the analyzer 202 or any digital devicemay analyze measurements collected over time to generate a health scorethat can be compared to a health threshold to provide qualitative and/orquantitative scoring. Similarly, the analyzer 202 or any digital devicemay analyze measurements recently collected to generate a current scorethat can be compared to a current health threshold to providequalitative and/or quantitative scoring.

In some embodiments, a user interface may display a health score 502, anoption for details regarding the health score 504, a current score 506,an option for details regarding the current score 508, a recommendation510, a settings option 512, and a history of measurements 514. Optionsfor details 504 and 506 may describe the metrics as well as the valuesof the metrics that went into the health score 504 and the current score506, respectively.

In some embodiments, there is a recommendation engine configured toretrieve recommendations 510 based on the health score 504 and/or thecurrent score 506. The settings option 512 may allow the user toconfigure metrics to be tracked and set alerts. In some embodiments, theuser may utilize the settings options 512 to secure the information(e.g., encrypt the information and/or set passwords). The history ofmeasurements option 514 may provide logged metrics and analysisinformation over time (e.g., as a chart).

Those skilled in the art will appreciate that the multispectral bloodmetrics measurement apparatus 200 and/or any digital device may generatereports based on the analysis, the metrics (e.g., blood metrics ormetrics based on nutrients), historic measurements, historic analysis,or any other information. Further, alerts may be set by themultispectral blood metrics measurement apparatus 200 and/or any digitaldevice.

Those skilled in the art will appreciate that the multispectral bloodmetrics measurement apparatus 200 may be taking many measurements overtime (e.g., many measurements every minute) and may track health andchanges in metrics over time and/or in the short term. In someembodiments, if a condition is of sufficient seriousness (e.g., heartrate shows erratic beats), the multispectral blood metrics measurementapparatus 200 or any digital device may provide an alert and requestassistance (e.g., from emergency personnel via the communicationnetwork).

Various health and wellness predictors such as, but not limited to,energy level, blood iron level, blood oxygen level, and blood glucoselevel are displayed. FIG. 6 illustrates a display 600 of an assessmentof an overall health index, derived from data collected from or with amultispectral blood metrics measurement apparatus in variousembodiments.

In some embodiments, a user interface may display a current score 602,energy balance information 606, sleep quality information 608, bloodmetrics information 610, and body composition information 612 as well asother information accessible by slider 604. Additional details may beavailable through buttons 614. Those skilled in the art will appreciatethat any amount of information may be provided. In some embodiments, thedisplay 600 summarizes information while more detailed informationrecommendations, measurement data, analysis information, and the likemay be available through the details buttons 614 or in other screens.

Recommendations to the user based on the current and previousmeasurements are provided. FIG. 7 illustrates a display 700 of anassessment of an overall health index, derived from data collected fromor with a multispectral blood metrics measurement apparatus in variousembodiments. In some embodiments, a user interface may display a currentscore 702, energy level information 706, blood iron level information708, blood oxygen level information 710, and blood glucose level 712 aswell as other information accessible by slider 704. Additional detailsmay be available through buttons 714. Those skilled in the art willappreciate that any amount of information may be provided. In someembodiments, the display 700 summarizes information while more detailedinformation recommendations, measurement data, analysis information, andthe like may be available through the details buttons 714 or in otherscreens.

Various embodiments track and analyze blood metrics. Healthrecommendations may be based on instantaneous blood metrics measurementsand history blood metrics measurement. In addition, blood metrics andhealth condition of a user may be compared to health data of the generalpublic. For example, a user's health condition may be compared to healthcondition of other similar users such as users of the same gender andage group, users of the same profession, friends of a user, etc.

FIG. 8 is a block diagram of an exemplary digital device 800. Thedigital device 800 comprises a processor 802, a memory system 804, astorage system 806, a communication network interface 808, an I/Ointerface 810, and a display interface 812 communicatively coupled to abus 814. The processor 802 is configured to execute executableinstructions (e.g., programs). In some embodiments, the processor 802comprises circuitry or any processor capable of processing theexecutable instructions.

The memory system 804 is any memory configured to store data. Someexamples of the memory system 804 are storage devices, such as RAM orROM. The memory system 804 can comprise the RAM cache. In variousembodiments, data is stored within the memory system 804. The datawithin the memory system 804 may be cleared or ultimately transferred tothe storage system 806.

The storage system 806 is any storage configured to retrieve and storedata. Some examples of the storage system 806 are flash drives, harddrives, optical drives, and/or magnetic tape. In some embodiments, thedigital device 800 includes a memory system 804 in the form of RAM and astorage system 806 in the form of flash data. Both the memory system 804and the storage system 806 comprise computer readable media which maystore instructions or programs that are executable by a computerprocessor including the processor 802.

The communications network interface (com. network interface) 808 can becoupled to a network (e.g., the computer network 104) via the link 816.The communication network interface 808 may support communication overan Ethernet connection, a serial connection, a parallel connection, oran ATA connection, for example. The communication network interface 808may also support wireless communication (e.g., 802.11a/b/g/n, WiMax). Itwill be apparent to those skilled in the art that the communicationnetwork interface 808 can support many wired and wireless standards.

The optional input/output (I/O) interface 810 is any device thatreceives input from the user and output data. The optional displayinterface 812 is any device that is configured to output graphics anddata to a display. In one example, the display interface 812 is agraphics adapter.

It will be appreciated by those skilled in the art that the hardwareelements of the digital device 800 are not limited to those depicted inFIG. 8. A digital device 800 may comprise more or less hardware elementsthan those depicted. Further, hardware elements may share functionalityand still be within various embodiments described herein. In oneexample, encoding and/or decoding may be performed by the processor 802and/or a co-processor located on a GPU (i.e., Nvidia®).

Some embodiments described herein include systems and methods fornon-invasive continuous blood pressure measurement. For example, a bloodmetrics measurement apparatus may generate multi-channel signals (e.g.,PPG signals) which may be provided to a blood pressure calculationsystem to calculate arterial blood pressure values (e.g., systolic bloodpressure value and/or diastolic blood pressure value). Morespecifically, the blood pressure calculation system (or the bloodpressure measurement apparatus) may filter the multi-channel signals(e.g., to remove noise from the signals), select (or, “extract”) subsetsof “high quality” waves from the multi-channel signals, select (or,“extract”) sets of features from each of the high quality waves, andgenerate sets of feature vectors based on the selected sets of features.In some embodiments, an empirical blood pressure model is used tocalculate arterial blood pressure values based on the sets of featurevectors.

FIG. 9 illustrates a diagram of a system and environment 900 fornon-invasive blood pressure measurement, in accordance with someembodiments. In some embodiments, the system and environment 900includes a blood metrics measurement apparatus 902, a user device 904, ablood metrics server 906, a communication network 908, and acommunication link 910.

The blood metrics measurement apparatus 902 may be configured tofacilitate the non-invasive measurement of a user's blood pressure. Insome embodiments, more particularly, the blood metrics measurementapparatus 902 facilitates non-invasive continuous measurement of auser's blood pressure. It will be appreciated that non-invasivecontinuous measurement may include measuring arterial blood pressure inreal time without interruption (e.g., without having to inflate anddeflate a cuff) and without inserting a device (e.g., a tube orcatheter) into to the user's tissue or body.

More specifically, the blood metrics measurement apparatus 902 mayproject energy into tissue of a user (e.g., the wearer of the apparatus902) and detect (or, “receive”) energy reflected from and/or transmittedthrough tissue of the user. In some embodiments, the blood metricsmeasurement apparatus 902 may project energy at one or more wavelengths(e.g., 523 nm, 590 nm, 623 nm, 660 nm, 740 nm, 850 nm, 940 nm, etc.)from multiple light sources (e.g., light-emitting diodes). The detectedenergy may be a fraction (or, “portion”) of the energy that is projectedinto the tissue. Energy at different wavelengths may be absorbed at adifferent rate that is related to a user's body state. The user's bodystate (e.g., heart rate, blood pressure, or the like) may determine theamount of absorbed energy. Accordingly, energy at different wavelengthsmay be absorbed at different levels by a user's body. The fraction ofenergy received (e.g., that is reflected by the tissue or transmittedthrough the tissue) may be used to generate signals, such asphotoplethysmogram (or, “PPG”) signals, at different levels. Thesesignals may provide information of the user's body state. Thisinformation may be obtained by analyzing waveforms of the signal in atime domain and/or a frequency domain.

A user may comfortably wear the blood metrics measurement apparatus 902over time. For example, the blood metrics measurement apparatus 902 maybe worn without interrupting typical user activity (e.g., moving,walking, running, sleeping, etc.). The blood metrics measurementapparatus 902 may comprise lightweight components. The blood metricsmeasurement apparatus 902 may be made of hypoallergenic materials. Theblood metrics measurement apparatus 902 may be flexibly built so that itcould fit various body parts (e.g., wrist, earlobe, ankle, or chest) ofa user. In some embodiments, the blood metrics measurement apparatus 902may include some or all of the functionality of the user device 904.

The user device 904 may include any digital device (e.g., mobile device)capable of executing an application related to measuring blood metrics,such as blood pressure calculation, presenting a user interface througha display and/or communicating with various entities in the examplesystem and environment 900 through the communication network 908 and/ora communication link 910 (discussed further below). For example, throughthe communication link 910, the user device 902 may receive one or moreblood metric measurements (e.g., one or more signals) from the bloodmetrics measurement apparatus 902, track and store the blood metricmeasurements, analyze the blood metric measurements, and/or providerecommendations and/or messages based on the blood metric measurements.An application user interface may facilitate interaction between a userof the user device 904 and an application running on the user device904.

In various embodiments, the user device 904 may perform analysis of themeasurements received from the blood metrics measurement apparatus 902(e.g., calculate blood pressure values), display results, providereports, display progress, display historic readings, trackmeasurements, track analysis, provide alerts (or, messages), and/or thelike.

The blood metrics server 906 may be configured to generate and/or storeempirical blood pressure models. For example, the blood metrics server906 may comprise one or more server computers, desktop computers, mobiledevices, and/or other digital device(s). In some embodiments, the bloodmetrics server 906 receives and process user registration requests(e.g., user account registration requests, blood metrics measurementapparatus registration requests, etc.), provides empirical bloodpressure model(s) to the user device 902 via the communication network908, and/or the like.

As used in this paper, computing devices (e.g., digital devices) mayinclude a mobile phone, a tablet computing device, a laptop, a desktopcomputer, personal digital assistant, a portable gaming unit, a wiredgaming unit, a thin client, a set-top box, a portable multi-mediaplayer, or any other type of network accessible user device known tothose of skill in the art. Further, the blood metrics server 908 maycomprise of one or more servers, which may be operating on orimplemented using one or more cloud-based services (e.g.,System-as-a-Service [SaaS], Platform-as-a-Service [PaaS], orInfrastructure-as-a-Service [IaaS]).

Each of the blood metrics measurement apparatus 902, the user device904, and the blood metrics server 906 may be implemented using one ormore digital devices. An example digital device is described in FIG. 8.

In some embodiments, the communication network 908 represents one ormore communication network(s). The communication network 908 may providecommunication between the blood metrics measurement apparatus 902, theuser device 904, and/or the blood metrics server 906. In some examples,the communication network 908 comprises digital devices, routers,cables, and/or other network topology. In other examples, thecommunication network 908 may be wireless and/or wireless. In someembodiments, the communication network 908 may be another type ofnetwork, such as the Internet, that may be public, private, IP-based,non-IP based, and so forth.

In some embodiments, the communication link 910 represents one or morecommunication network connections. The communication link 910 mayprovide communication between the blood metrics measurement apparatus902 and the user device 904. In some examples, the communication link910 comprises a network connection of the communication network 908,and/or a separate communication network. In some embodiments, thecommunication link 910 comprises a wireless communication link, such asa Bluetooth communication link, Wi-Fi communication link, and/or thelike.

FIG. 10 depicts a block diagram 1000 of a blood metrics measurementapparatus 902 according to some embodiments. The blood metricsmeasurement apparatus 902 comprises an analyzer 1002, an energytransmitter 1004, an energy receiver 1006, and a communication module1008. Various embodiments may comprise a wearable member. The wearablemember may include, for example, a bracelet, glasses, necklace, ring,anklet, belt, broach, jewelry, clothing, or any other member ofcombination of members that allow the blood metrics measurementapparatus 902 to be close to or touch a body of the wearer.

The energy transmitter 1004 and the energy receiver 1006 may be securedto the wearable member such that the energy transmitter and the energyreceiver may make contact or be in proximity with tissues (e.g., skin)of a user. The analyzer 1002 may be coupled to the energy transmitter1004 and the energy receiver 1006. In further embodiments, the bloodmetrics measurement apparatus 902 may comprise a communication module(not shown). The communication module may be coupled to the analyzer1002. The blood metrics measurement apparatus 902 may further comprise adriver (not shown) and a power source (not shown). The driver may becoupled to the energy transmitter 1004 and the analyzer 1002. Theanalyzer 1002 may be coupled to the energy transmitter 1004 via thedriver. The power source may be coupled to the energy transmitter 1004via the driver. The blood metrics measurement apparatus 902 may furthercomprise an Analog-to-Digital Converter (“ADC”) (not shown). The ADC maybe coupled to the energy receiver 1006 and the analyzer 202. In someembodiments, the blood metrics measurement apparatus 902 may comprise amotion sensor (e.g., an accelerometer, a global positioning system) (notshown). The motion sensor may be coupled to the analyzer 1002.

In various embodiments, the energy transmitter 1004 emits energyincluding, but not limited to, light, into the body of the user. Theenergy produced by the energy transmitter may be in the direction ofentering tissues. For example, the energy produced by the energytransmitter 1004 is in a direction 1051 entering the tissue 1010. Insome embodiments, the energy transmitter 1004 emits energy or light atdifferent wavelengths. The energy transmitter 1004 may comprise anynumber of light emission diodes (“LEDs”). In some embodiments, theenergy transmitter 1004 comprises at least two LEDs. Each LED may beconfigured to emit energy at one or more wavelengths. In anotherexample, each LED may emit light with a peak wavelength centered arounda wavelength. In one example, the energy transmitter 1004 may emit lightwith a peak wavelength centered around 500 nm to 1800 nm.

Each wavelength may correspond to one or more blood metrics of interestand/or one or more nutrients. Those skilled in the art will appreciatethat different components of the blood and/or different nutrients mayabsorb energy at different wavelengths. In various embodiments, acontroller, driver, analyzer 1002, or the like may receive a bloodmetric or nutrient of interest (e.g., from a user of the blood metricsmeasurement apparatus 902 and/or a user device not shown). Thecontroller, driver, analyzer 1002 or the like may associate the bloodmetric and/or nutrient of interest with one or more wavelengths andconfigure one or more of the LEDs to emit energy of at least one of theone or more wavelengths. For example, the analyzer 1002 may command thedriver to deliver electric power to one LED that is configured to emitlight at the desired wavelength.

The energy receiver 1006 may detect energy associated with the energyprovided by the LEDs from tissues (e.g., skin) of the user. In thisexample, received and/or detected energy is in the direction 1052 thatleaves from the tissue 1010. In various embodiments, the energy receiver1006 may detect energy from the body of the user that is a fraction ofthe energy produced by the energy transmitter 1004.

The energy transmitter 1004 and the energy receiver 1006 may beconfigured such that the energy receiver 1006 detects reflected energyfrom tissues of the user of the multispectral blood metrics measurementapparatus 902. For example, the energy transmitter 1004 and the energyreceiver 1006 may be configured to be disposed on one surface or side ofa user's tissue. The energy transmitter 1004 and the energy receiver1006 may be configured such that the energy receiver 1006 detects energyfrom the energy transmitter 1004 that passes through or reflects fromthe user's tissues. In some embodiments, the energy transmitter 1004 andthe energy receiver 1006 may be configured to be disposed on different(e.g., opposite) surfaces or sides of a users' tissue.

Energy detected from tissues of a user may be detected by the energyreceiver 1006. The energy receiver 1006 may be configured to generate asignal in response to the detected energy. In some embodiments, theenergy receiver 1006 may be triggered by the energy received to generatean output which may be dependent or partially dependent upon the amountof energy received. The energy receiver 1006 may be configured togenerate a signal (e.g., an electric current, or an electric voltage) inresponse to the energy received from the tissues.

The signal generated by the energy receiver 1006 may be associated withone or more blood metrics and/or nutrients of interest. Energy atdifferent wavelengths may be absorbed at a different rate that isrelated to a user's body state. The user's body state (e.g., heart rate,blood pressure, nutrient level, or the like) may determine the amount ofenergy absorbed by the body. Accordingly, energy from the user's body atdifferent wavelengths may be detected at different levels therebycausing different responses of the energy receiver 1006. The energyreceiver 1006 may, for example, output signals based on the level of theenergy received.

The energy receiver 1006 may provide information associated with theuser's body state. Blood metric information may be determined (e.g., bythe analyzer 1002) from the output signal of the energy receiver 1006.

The energy receiver 1006 may comprise a set of photodetectors (e.g., aphoto diode, or a photo transistor) which are configured to output asignal dependent upon photons or the like from the energy transmitter1004 that passed through tissues of the user.

In various embodiments, the output signal of the energy receiver 1006 isa composite of multiple signals. Each signal of the composite may beassociated with energy at a wavelength which may be a portion (orfraction) of the total energy emitted by the energy transmitter 1004.

The energy transmitter 1004 may be configured to generate energy at aset of wavelengths. In some embodiments, the energy transmitter 1004 isconfigured to generate energy such that energy at different wavelengthsis generated sequentially and/or periodically. The energy transmitter1004 may be configured to generate energy at each particular wavelengthuntil energy at all wavelengths of the set is generated. The period oftime for the energy transmitter 1004 to generate energy at allwavelengths is a generation period. Subsequent to completion of thegeneration period, the energy transmitter 1004 may start a newgeneration period thereby allowing multiple measurements.

The communication module 1008 may be configured to send requests to andreceive data from one or a plurality of systems. The communicationmodule 1008 may send requests to and receive data from a systems througha network or a portion of a network. Depending uponimplementation-specific or other considerations, the communicationmodule 1008 may send requests and receive data through a connection(e.g., the communication link 910), all or a portion of which may be awireless connection. The communication module 1008 may request andreceive messages, and/or other communications from associated systems.

FIG. 11 shows a flowchart 1100 of an example method of operation of ablood metrics measurement apparatus (e.g., blood metrics measurementapparatus 902) according to some embodiments. In this and otherflowcharts described herein, the flowchart illustrates by way of examplea sequence of steps. It should be understood the steps may bereorganized for parallel execution, or reordered, as applicable.Moreover, some steps that could have been included may have been removedto avoid providing too much information for the sake of clarity and somesteps that were included could be removed, but may have been includedfor the sake of illustrative clarity.

In step 502, the blood metrics measurement apparatus projects energyinto tissue of a user (e.g., the user wearing blood metrics measurementapparatus). The energy may be projected from an energy transmitter(e.g., energy transmitter 1004) comprising a plurality of light sources(e.g., LEDs). In some embodiments, a first light source (e.g., one ormore LEDs) may project light energy at a plurality of differentwavelengths, such as 523 nm, 590 nm, 623 nm, 660 nm, 740 nm, 850 nm, and940 nm, and a second light source (e.g., one or more LEDs) may projectenergy at the same, or substantially similar, wavelength as one of thewavelengths projected by the first light source, e.g., 523 nm, 590 nm,623 nm, 660 nm, 740 nm, 850 nm, or 940 nm. It will be appreciated thatother configuration may be used, e.g., a greater number of lightsources, a greater or lesser number of wavelengths projected from thelights sources, and so forth.

In step 504, the blood metrics measurement apparatus receives (or,“detects) portions of energy through the tissue of the user. In someembodiments, an energy receiver (e.g., energy receiver 1006) detects aportion of the energy transmitted into the user's tissue by the energytransmitter. The energy receiver may generate a signal based on theportion of energy detected (e.g., based on the amount of the energydetected). For example, energy detected may be a portion of the energygenerated at step 502 reflected by the tissue. Energy detected may be aportion of the energy generated at step 302 that passes through thetissue (e.g., other undetected energy may be absorbed by tissue and/orotherwise blocked). In various embodiments, steps 502 and 504 areperformed simultaneously or substantially simultaneously. That is,energy generation and detection may be performed approximatelysimultaneously.

In step 506, the blood metrics measurement apparatus generates one ormore signals based on the received portions of energy. In someembodiments, the energy receiver may generate a multi-channel PPG signal(e.g., as mentioned above). The output (or, “generated”) signal of theenergy receiver may be an electric current or an electric voltage, ofwhich the amplitude may be related to the amount of the energy detected.

In various embodiments, analysis of the signals from the energy receivermay identify abnormal measurements. For example, each of themeasurements may be compared to a predetermined value. If the differencebetween the measurement and the predetermined value is above (or below)a threshold, then the measurement may be determined to be abnormal. Anabnormal value may trigger additional analysis or an alert. In someembodiments, an abnormal value is ignored (e.g., as possibly effected bynoise caused by movement of the energy transmitter and/or the energyreceiver). In various embodiments, the abnormal value may be discounted(e.g., the weight of the value reduced). The degree of discount may bebased, for example, on information from an accelerometer (e.g., a largeacceleration may indicate that the abnormal value should besignificantly discounted) and/or based on historical values. Thoseskilled in the art will appreciate that the degree of discount may bebased on any number of factors.

In some embodiments, measurements may be averaged over a period of time.A Kalman filer (e.g., a nonlinear, unscented Kalman filter) may beapplied to any number of measurements or averaged measurements. A motionmeasurement (e.g., a measurement by an accelerometer) may be considered.Upon determining a measurement is abnormal, the motion measurement forthat time point may be inspected. A large measurement may indicate largevibrations or accelerations that corroborate that the measurement may beabnormal. Measurements collected in such situations are likely to havesignificant electrical noises.

At step 508, the analyzer (e.g., analyzer 1002) may analyze signals fromthe energy receiver analyzed in the frequency domain to determine bloodmetrics. Concentration of a nutrient in the blood may subsequently bedetermined. In some embodiments, signals may be provided to a bandpassfilter that separates AC components from DC components. An AC componentmay represent signal variation at the cardiac frequency and a DCcomponent may represent the average overall transmitted light intensity.In some embodiments, a heart rate and/or oxygen saturation, SpO₂ may bedetermined. The heart rate may be determined, for example, by averagingthe maximum frequency to determine the rate of cardiac beats in apredetermined amount of time. The oxygen saturation SpO₂ may bedetermined according to Equation (1):S_(p)O₂=110−25×R  (1),

where R is the ration of a red and infrared normalized transmitted lightintensity. R may be determined according to Equation (2):

$\begin{matrix}{{R = \frac{{AC}_{R}/{DC}_{R}}{{AC}_{IR}/{DC}_{IR}}},} & {(2),}\end{matrix}$where the AC_(R) is the AC component of the detected energycorresponding to a wavelength (e.g., red light), DC_(R) is the DCcomponent of the detected energy corresponding to the wavelength (e.g.,red light), AC_(IR) is the AC component of the detected energycorresponding to a different wavelength (e.g., infrared light), andDC_(IR) is the DC component of the detected energy corresponding to thedifferent wavelength (e.g., infrared light). In some embodiments, the ACcomponent may be selected as the highest spectral line in the cardiacfrequency band. Waveform analysis may be performed to determine the R-Rinterval defined by two successive AC components, an elapsed intervaland the probation, if there is any.

Those skilled in the art will appreciate that analysis may be performedby the analyzer and/or any other digital device (e.g., user device or ablood metrics server, e.g., blood metrics server 906).

State space estimation and progression may be performed to determineblood metrics. A system may be modeled according to Equation (3):x(n+1)=f[x(n)]+u(n)y(n)=h[x(n)]+v(n)  (3),where x(n) represents the state of the system, u(n) is process noise,y(n) is the vector of the observed signals, and v(n) is the measurementnoise.

Table 1 lists one or more parameters for x(n) as well as their initialvalue in some embodiments:

TABLE 1 Parameter Symbol Initial Value Cardiac frequency ƒ_(HR) 1 HzCardiac phase θ_(HR) 0 Cardiac harmonic I_(Harmonic) ^(HR) 0 amplitudeCardiac Pulse P_(HR) 1 Pressure Point Blood Pressure P_(Point) 1Respiratory ƒ_(Resp) 0.3 Hz frequency Respiratory phase θ_(Resp) 0Wavelength i = 1 . . . N I_(λ) _(i) ^(AC) 0.5 max_value AC peakamplitude Wavelength i = 1 . . . N pos_(λ) _(i) ^(AC) Corresponding FFTAC peak location bin to 1 Hz Wavelength i = 1 . . . N I_(λ) _(i) ^(DC)0.5 max_value DC Wavelength i = 1 . . . N I_(λ) _(i) ^(p2p) 1 ADC readp2p amplitude Wavelength i = 1 . . . N τ_(λ) _(i) ^(rise) 0.1 sec risetime Wavelength i = 1 . . . N c_(λ) _(i) 1 Significance coefficientWavelength i = 1 . . . N T_(λ) _(i) ^(HRV)   1 sec HRV Best Ratio pHBR_(pH) 2 Best Ratio pCO2 BR_(pCO2) 3 Best Ratio pHCO3− B R_(pHCO3) ⁻ 4Acceleration I_(move) 0 magnitude GPS velocity |v|_(GPS) 0 GPS altitude|alt|_(GPS) 0 GPS acceleration |a|_(GPS) 0 GPS incline |incline|_(GPS) 0Restfulness Rest 0 Hydration Hyd 0 Systolic Blood SBP 120 mmHg PressureDiastolic Blood DBP  80 mmHg Pressure End tidal CO2 ETCO2  40 mmHg BloodCarbon SpCO 0% Monoxide

Table 2 lists one or more parameters for y(n) as well as their initialvalue in some embodiments:

TABLE 2 Parameter Symbol Initial Blood pH pH 7.35 Blood PCO2 pCO₂ 24mmol Blood PO2 pO₂ 24 mmol Blood PHCO3− pHCO₃ ⁻ 24 mmol Blood GlucosepC₆H₁₂O₆  3 mmol Cardiac Frequency ƒ_(HR) 1 Point Blood PressureP_(Point) 1 Respiratory ƒ_(Resp) 0.3 Frequency GPS velocity |v|_(GPS) 0GPS altitude |alt|_(GPS) 0 GPS acceleration |a|_(GPS) 0 GPS incline|incline|_(GPS) 0

Table 3 lists the state space model F(X(n)) between the parameterslisted in Table 1 and Table 2 in some embodiments, where the energywavelengths comprise 880 nm, 631 nm, 1450 nm, and 1550 nm:

TABLE 3 Name Symbol Equation Cardiac frequency f_(HR)${bin\_ to}{\_ freq}\left( \frac{\sum{c_{\lambda_{i}}{pos}_{\lambda_{i}}^{AC}}}{\sum c_{\lambda_{i}}} \right)$Cardiac θ_(HR) θ_(HR)(n − 1) + f_(s) ⁻¹ * ω*, where ω* ε [ω_min, ω_max]phase Cardiac harmonic amplitude I_(Harmonic) ^(HR)$\frac{\sum{c_{\lambda_{i}}I_{\lambda_{i}}^{p2p}}}{\sum c_{\lambda_{i}}}$Cardiac Pulse Pressure P_(HR)$\left( \frac{\sum{c_{\lambda_{i}}\tau_{\lambda_{i}}^{rise}}}{\sum c_{\lambda_{i}}} \right)\bigwedge{- 1}$Point Blood P_(Point) τ_(λ) ₁ ^(rise) ⁻¹ Pressure Respiratory f_(Resp)3) Respiratory and Heart Rate State Models: The fluctuations frequencyin the respiratory rate ω_(r)(n) and fluctuations in the heart rateω_(ca)(n) that are not due to RSA are both modeled as a first-orderautoregressivc process with a mean and mild nonline- arity that limitthe frequencies to know physiologic ranges ω_(r)(n + 1) = ω _(r) + α_(r){s_(r) [ω_(r)(n)] − ω _(r)} + u_(ω) _(r) (n) (15) ω_(ca)(n + 1) = ω_(c) + α_(c) {s_(c) [ω_(ca)(n)] − ω _(c)} + u_(ω) _(ca) (n) (16) where ω_(r) and ωc are the a priori estimates of the expected respiratory andcardiac frequencies, respectively; α_(r) and α_(c) con- trol thebandwidth of the frequency fluctuations; and u_(ω) _(r) (n) and u_(ω)_(ca) (n) are white noise processes that model the random vari- ation inthe respiratory and cardiac frequencies, respectively. The instantaneousrespiratory and heart rates in units of Hz are then${f_{r}(n)} = {\frac{1}{2{\pi T}_{s}}{s_{r}\left\lbrack {\omega_{r}(n)} \right\rbrack}}$(17)${f_{c}(n)} = {\frac{1}{2{\pi T}_{s}}{{s_{c}\left\lbrack {\omega_{c}(n)} \right\rbrack}.}}$(18) Respiratory θ_(Resp) θ_(Resp)(n − 1) + f_(s) ⁻¹ * ω*, where ω* ε[ω_min, ω_max] phase λ = 880 nm I_(λ) _(i) ^(AC) From FFT AC peak λ =880 nm pos_(λ) _(i) ^(AC) From FFT DC λ = 880 nm I_(λ) _(i) ^(DC) FromWaveform analysis p2p amplitude λ = 880 nm I_(λ) _(i) ^(p2p) FromWaveform analysis rise time λ = 880 nm τ_(λ) _(i) ^(rise) From Waveformanalysis signal trend λ = 880 nm c_(λ) _(i) From Waveform analysisSignificance coefficient λ = 880 nm T_(λ) _(i) ^(HRV) From Waveformanalysis HRV λ = 631 nm I_(λ) _(i) ^(AC) From Fast FourierTransformation (“FFT”) AC peak λ = 631 nm pos_(λ) _(i) ^(AC) From FFT DCλ = 631 nm I_(λ) _(i) ^(DC) From Waveform analysis p2p amplitude λ = 631nm I_(λ) _(i) ^(p2p) From Waveform analysis rise time λ = 631 nm τ_(λ)_(i) ^(rise) From Waveform analysis signal trend λ = 631 nm c_(λ) _(i)From Waveform analysis Significance coefficient λ = 631 nm T_(λ) _(i)^(HRV) From Waveform analysis HRV λ = 1450 nm I_(λ) _(i) ^(AC) From FFTAC peak λ = 1450 nm pos_(λ) _(i) ^(AC) From FFT DC λ = 1450 nm I_(λ)_(i) ^(DC) From Waveform analysis p2p amplitude λ = 1450 nm I_(λ) _(i)^(p2p) From Waveform analysis rise time λ = 1450 nm τ_(λ) _(i) ^(rise)From Waveform analysis signal trend λ = 1450 nm c_(λ) _(i) From Waveformanalysis Significance coefficient λ = 1450 nm T_(λ) _(i) ^(HRV) FromWaveform analysis HRV λ = 1550 nm I_(λ) _(i) ^(AC) From FFT AC peak λ =1550 nm pos_(λ) _(i) ^(AC) From FFT DC λ = 1550 nm I_(λ) _(i) ^(DC) FromWaveform analysis p2p amplitude λ = 1550 nm I_(λ) _(i) ^(p2p) FromWaveform analysis rise time λ = 1550 nm τ_(λ) _(i) ^(rise) From Waveformanalysis signal trend λ = 1550 nm c_(λ) _(i) From Waveform analysisSignificance coefficient λ = 1550 nm T_(λ) _(i) ^(HRV) From Waveformanalysis HRV Best Ratio B R_(pH) Device Calibration pH Best Ratio BR_(pCO2) Device Calibration pCO2 Best Ratio B R_(pHCO3)− DeviceCalibration pHCO3− Acceleration I_(move) From Accelerometer magnitudeGPS velocity |v|_(GPS) From GPS GPS altitude |alt|_(GPS) From GPS GPS|a|_(GPS) From GPS acceleration GPS incline |incline|_(GPS) From GPS

Table 4 lists Y(n)=H(x(n)):

TABLE 4 Name Symbol Equation Blood pH pH$6.1 + {\log\left( \frac{{pHCO}_{3}^{-}}{0.03{pCO}_{2}} \right)}$ BloodPCO2 pCO₂$\frac{\varepsilon_{Hb}^{{CO}_{2}} - {\varepsilon_{Hb}^{Hb}*I_{\lambda_{{CO}_{2}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{{CO}_{2}}}^{D\; C}} \right)}}}{\varepsilon_{Hb}^{{CO}_{2}} - \varepsilon_{{CO}_{2}}^{{CO}_{2}} + {\left( {\varepsilon_{{CO}_{2}}^{Hb} - \varepsilon_{Hb}^{Hb}} \right)*I_{\lambda_{{CO}_{2}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{{CO}_{2}}}^{D\; C}} \right)}}}$Blood PO2 pO₂$\frac{\varepsilon_{Hb}^{O_{2}} - {\varepsilon_{Hb}^{Hb}*I_{\lambda_{O_{2}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{O_{2}}}^{D\; C}} \right)}}}{\varepsilon_{Hb}^{O_{2}} - \varepsilon_{O_{2}}^{O_{2}} + {\left( {\varepsilon_{O_{2}}^{Hb} - \varepsilon_{Hb}^{Hb}} \right)*I_{\lambda_{O_{2}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{O_{2}}}^{D\; C}} \right)}}}$Blood PHCO3− pHCO₃ ⁻$\frac{\varepsilon_{Hb}^{{HCO}_{3}^{-}} - {\varepsilon_{Hb}^{Hb}*I_{\lambda_{{HCO}_{3}^{-}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{{HCO}_{3}^{-}}}^{D\; C}} \right)}}}{\varepsilon_{Hb}^{{HCO}_{3}^{-}} - \varepsilon_{{HCO}_{3}^{-}}^{{HCO}_{3}^{-}} + {\left( {\varepsilon_{{HCO}_{3}^{-}}^{Hb} - \varepsilon_{Hb}^{Hb}} \right)*I_{\lambda_{{HCO}_{3}^{-}}}^{A\; C}*{I_{\lambda_{1}}^{D\; C}/\left( {I_{\lambda_{1}}^{A\; C}*I_{\lambda_{{HCO}_{3}^{-}}}^{D\; C}} \right)}}}$Blood Glucose pC₆H₁₂O₆ As above Cardiac f_(HR) As in f(x(n)) FrequencyPoint Blood P_(Point) As in f(x(n)) Pressure Respiratory f_(Resp) As inf(x(n)) Frequency GPS velocity |v|_(GPS) As in f(x(n)) GPS altitude|alt|_(GPS) As in f(x(n)) GPS |a|_(GPS) As in f(x(n)) acceleration GPSincline |incline|_(GPS) As in f(x(n))

As illustrated in Tables 3 and 4, by generating energy at differentwavelengths, one or more blood metrics may be determined from thedetected energy. For example, cardiac frequency, cardiac phase, cardiacharmonic amplitude, cardiac pulse pressure, point blood pressure,respiratory frequency, respiratory phase, blood pH, blood pCO₂, bloodpHCO₃₋, or blood glucose, may be determined.

In step 508, the blood metrics measurement apparatus provides thegenerated one or more signals for blood pressure calculation. In someembodiments, a communication module (e.g., communication module 1008)provides the one or more signals, e.g., to a blood pressure calculationsystem and/or the user device.

FIG. 12 depicts a block diagram 1200 of a user device 904 according tosome embodiments. Generally, the user device 904 may be configured todisplay, or otherwise present blood pressure values, messages, alerts,and/or the like. The user device 904 may also provide registrationfeatures allowing a user to register a blood metrics measurement device902, create and/or update a user account, and communicate with othersystems of the system and environment 900. In some embodiments, the userdevice 904 includes a user interface module 1202, a registration module1204, a blood pressure calculation system 1206, and a communicationmodule 1208.

The user interface module 1202 may be configured to present imagesand/or audio corresponding to health data, such as blood pressurevalues, messages, alerts, and the like. For example, the user interfacemodule 1202 may display one or more graphical user interfaces (GUIs) topresent a calculated blood pressure to a user. Example user interfacesare described further with reference to FIG. 5-7, discussed above.

The registration module 1204 may be configured to generate registrationrequests to create, read, update, delete, or otherwise access,registration records associated with user accounts (e.g., a user accountassociated with a user of the user device 904 and/or the blood metricsmeasurement apparatus 902) and registration records associated with theblood metrics measurement apparatus 902. In some embodiments, a userinputs user account registration information and blood metricsmeasurement apparatus registration information via the user interfacemodule 1202. For example, user account registration information mayinclude geographic attributes, demographic attributes, psychographicattributes, and/or behavioristic attributes. Accordingly, user accountregistration information may include some or all of the followingattributes:

-   -   User Account Identifier: Identifier that identifies a user        account.    -   Password: Password, or other personal identifier, used to        authenticate the user account. For example, it may an        alphanumerical password, biometric data (e.g., fingerprint,        etc.). In some embodiments, readings or measurements from the        blood metrics measurement apparatus 902 may be used to        authenticate the user account.    -   Device Identifier(s): Identifier(s) that identify one or more        blood metric measurement apparatus' associated with the user        account.    -   Name: A name of the user.    -   DOB: A date of birth of the user.    -   Age: An age of the user.    -   Gender: Gender of the user, e.g., female, male, transgender,        etc.    -   Weight: A weight of the user.    -   Height: A height of the user.    -   Skin color: A skin color of the user.    -   Activity Level: An activity level of the user, e.g., sedentary,        lightly active, active, very active, and so forth.    -   Geographic location: A location of the user, e.g., as determined        by a location service and/or specified by the user.    -   Blood Pressure Profile: Hypertensive, Hypotensive, Normal or        unknown.    -   Blood Glucose Profile (e.g., Diabetes information)    -   Wrist circumference: Circumference of the user's wrist.

In some embodiments, the blood metrics measurement apparatusregistration information includes some or all of the followingattributes:

-   -   Apparatus Identifier: Identifier that identifies a blood metrics        measurement apparatus.    -   User Account Identifier: Identifier that identifies a user        account associated with the blood metrics measurement apparatus.    -   Geographic location: A current location of the blood metrics        measurement apparatus, e.g., as determined by a location service        and/or specified by the user.    -   Settings: One or more settings of the blood measurement metrics        apparatus. For example, some or all of the settings may be        automatically determined based on one or more user account        attributes (e.g., height, weight, etc.) and/or by the user.

The blood pressure calculation system 1206 may be configured tocalculate blood pressure values (e.g., systolic, diastolic), andgenerate messages or alerts based on those values. An example of theblood pressure calculation system 1206 is discussed further below withreference to FIG. 14.

The communication module 1208 may be configured to send requests to andreceive data from one or a plurality of systems. The communicationmodule 1208 may send requests to and receive data from a systems througha network or a portion of a network. Depending uponimplementation-specific or other considerations, the communicationmodule 1208 may send requests and receive data through a connection(e.g., the communication network 908, and/or the communication link910), all or a portion of which may be a wireless connection. Thecommunication module 1208 may request and receive messages, and/or othercommunications from associated systems.

FIG. 13 shows a flowchart 1300 of an example method of operation of auser device (e.g., user device 904) according to some embodiments.

In step 1302, the user device registers a blood metrics measurementapparatus (e.g., blood metrics measurement apparatus 902). In someembodiments, input from a user is received by a user interface module(e.g., user interface module 1202) which triggers a registration module(e.g., registration module 1204) to generate a registration request toassociate the blood metrics measurement apparatus with a user and/or theuser device. The registration request may include, for example, one ormore blood metrics measurement apparatus attributes. In someembodiments, a communication module (e.g., communication module 1208)provides the registration request to a server (e.g., blood metricsserver 906) for processing.

In step 1304, the user device receives one or more signals from theregistered blood metrics measurement apparatus. In some embodiments, theone or more signals comprise a multi-channel PPG signal. In someembodiments, the one or more signals may be received by thecommunication module.

In step 1306, the user device calculates one or more arterial bloodpressure values (e.g., systolic values and/or diastolic values) based onthe received one or more signals. In some embodiments, a blood pressurecalculation system (e.g., blood pressure calculation system 1206)calculates the one or more arterial blood pressure values. Although thisexample shows the user device calculating the one or more arterial bloodpressure values, it will be appreciated that one or more other systemshaving the functionality of a blood pressure calculation system mayperform the calculation. For example, in some embodiments, the bloodpressure measurement apparatus and/or the server may include suchfunctionality and perform the calculation.

In step 1308, the user device presents a blood pressure message to theuser based on at least one of the one or more calculated arterial bloodpressure values. For example, the message may include some or all of thearterial blood pressure values, alerts (e.g., high BP, low BP, good BP,poor BP, etc.) based on one or more of the calculated values, and soforth. In some embodiments, the user device presents (e.g., via images,audio, vibrations, etc.) the blood pressure message or alert to the uservia the user interface module, or other feature of the user device.

FIG. 14 depicts a block diagram 1400 of a blood pressure calculationsystem 1206 according to some embodiments. Generally, the blood pressurecalculation system 1206 may be configured to calculate arterial bloodpressure values of a user. The blood pressure calculation system 1206may also store calculated arterial blood pressure values (e.g., forhealth tracking, etc.), and communicate with other systems of the systemand environment 900. In some embodiments, the blood pressure calculationsystem 1206 includes a management module 1402, a signal database 1404, awave database 1406, a wave feature database 1408, a feature vectordatabase 1410, a blood pressure model database 1412, a blood pressureresults database 1414, a rules database 1416, a wave selection module1418, a feature extraction module 1420, a blood pressure processingmodule 1422, and a communication module 1424.

The management module 1402 may be configured to manage (e.g., create,read, update, delete, or access) signal records 1426 stored in thesignal database 1404, wave records 1428 stored in the wave database1406, wave feature records 1430 stored in the wave feature database1408, feature vector records 1432 stored in the feature vector database1410, empirical blood pressure model records 1434 stored in the bloodpressure model database 1412, blood pressure result records 1436 storedin the blood pressure results database 1414, and/or rules 1438-1442stored in rules database 1416. The management module 1402 may performthese operations manually (e.g., by an administrator interacting with aGUI) and/or automatically (e.g., by one or more of the modules1418-1422). In some embodiments, the management module 1402 comprises alibrary of executable instructions which are executable by a processorfor performing any of the aforementioned management operations. Thedatabases 1404-1416 may be any structure and/or structures suitable forstoring the records 1426-1436 and/or the rules 1438-1442 (e.g., anactive database, a relational database, a table, a matrix, an array, aflat file, and the like).

The signal records 1426 may include a variety of signals, along withassociated metadata. For example, the signals may comprisesingle-channel and/or multi-channel PPG signals. In some embodiments,the metadata may include information obtained from the signals, such asheart rate(s) of an associated user. For example, the signal records1426 may store some or all of the following information:

-   -   Signal(s) Identifier: Identifier that identifies the stored        signal(s).    -   Signal(s): one or more signals (e.g., PPG Signals). The signals        may be raw signals (e.g., as detected by the associated blood        pressure measurement apparatus), filtered signals (e.g., to        remove noise from the signals), and/or normalized signal values        (e.g., between 0-1). An example “noisy” (i.e., unfiltered) PPG        signal and an example filtered PPG signal are shown in FIG. 21.        It will be appreciated that as used in this paper, a “signal,”        such as a PPG signal, generally refers to a filtered signal,        although in some embodiments, it may also refer to an unfiltered        signal instead of, or in addition to, the filtered signal.    -   Set(s) of Waves: one or more sets of waves of a predetermined        time series (e.g., 8 seconds) of the signal(s). The set of waves        may include raw waves, filtered waves, and/or normalized wave        values (e.g., between 0-1). An example set of waves is shown in        FIG. 22.    -   Apparatus Identifier: Identifier that identifies the blood        metrics measurement apparatus that generated the signals.    -   User Account Identifier: Identifier that identifies a user        account associated with the blood metrics measurement apparatus        that generated the signals.    -   Metadata: Metadata obtained from the signals, such as heart rate        or other biometric data. The metadata may also include other        information of the user, such as gender, age, height, weight,        skin color, e.g., obtained from the user's account information.        Such metadata values may be used by the blood pressure        calculation module 1422 (discussed below) to facilitate        calculation of arterial blood pressure values. In some        embodiments, metadata values may be provided to an empirical        blood pressure model (discussed below) via sets of feature        vectors (discussed below) and/or be provided separately to the        model.

The wave records 1428 may include sets of waves of a signal (e.g., asignal stored in the signal database 1404), along with subsets of thosewaves. The subsets of waves may comprise “high quality” waves obtainedfrom the waves of the signal. These subsets of waves may provide, forexample, a more accurate blood pressure calculation that just using thesignal or waves of the signal. In some embodiments, the wave records1428 may store some or all of the following information:

-   -   Signal Identifier: Identifier that identifies an associated        signal.    -   Wave Identifier(s): Identifiers for subsets of waves of the        associated signal.    -   Subset(s) of Waves: one or more subsets of waves of the        associated signal. The subsets of waves may include raw waves,        filtered waves, and/or normalized wave values (e.g., between        0-1). The subsets of waves may be referred to as “high quality”        waves. An example wave of a subset of waves is shown in FIG. 22.    -   Apparatus Identifier: Identifier that identifies the blood        metrics measurement apparatus that generated the signals.    -   User Account Identifier: Identifier that identifies a user        account associated with the blood metrics measurement apparatus        that generated the signals.

The wave feature records 1430 may include wave features of associatedsubsets of waves of a signal. For example, wave features may includewave peaks, wave valleys, wave edges, and/or the like. In someembodiments, the wave feature records 1430 may store some or all of thefollowing information:

-   -   Signal Identifier: Identifier that identifies an associated        signal.    -   Wave Identifier(s): Identifiers for the subsets of waves of the        associated signal.    -   Wave Features: one or more features obtained from the waves        within the associated subsets of waves. The wave features may        include points of the waves, such as wave peaks, wave valleys,        wave edges, and/or the like. The wave features may be stored as        normalized wave values (e.g., between 0-1). Example wave        features are shown in FIG. 23.    -   Apparatus Identifier: Identifier that identifies the blood        metrics measurement apparatus that generated the signals.    -   User Account Identifier: Identifier that identifies a user        account associated with the blood metrics measurement apparatus        that generated the signals.

The feature vector records 1432 may include sets of features generatedbased on the wave features of associated subsets of waves of a signal.In some embodiments, the feature vector records 1432 may store some orall of the following information:

-   -   Signal Identifier: Identifier that identifies an associated        signal.    -   Wave Identifier(s): Identifiers for the subsets of waves of the        associated signal.    -   Set(s) of Feature Vectors: one or more sets of feature vectors,        each feature vector comprising features extracted from a wave of        an associated subset of waves. The values of a feature vector        may include measurement values and metric values. For example,        the measurement values may correspond to amplitude or location        points of a particular wave, and the metric values may be        generated from metric functions that use at least one of the        measurement values. The values of a feature vector may comprise        normalized values (e.g., between 0-1). An example feature vector        2400 is shown in FIG. 24.    -   Apparatus Identifier: Identifier that identifies the blood        metrics measurement apparatus that generated the signals.    -   User Account Identifier: Identifier that identifies a user        account associated with the blood metrics measurement apparatus        that generated the signals.

The blood pressure model records 1434 may include one or more empiricalblood pressure models (e.g., retrieved from the blood metrics server906). The models may include various types of empirical blood pressuremodels. For example, a first type may be a “non-specific” model whichdoes not require calibration in order to be used to calculate arterialblood pressure values. A second type may be a “specific” model whichrequires calibration in order to be used to calculate arterial bloodpressure values. For example, models of the second type may requireinformation about the user, such age, weight, height, gender, skincolor, and/or the like. In some embodiments, the blood pressure records1434 may store some or all of the following information:

-   -   Model Identifier: Identifies an empirical blood pressure model.    -   Model Type: Identifies a type of model, e.g., non-specific or        specific.    -   Model Parameters: Various model parameters (e.g., decision node        parameters) and tree structures used to calculate the arterial        blood pressure values based on the sets of feature vectors        and/or other related information (e.g., gender, age, weight,        height, skin color, etc.). Example tree structures are shown in        FIG. 26.    -   Apparatus Identifier(s): Identifier(s) that identify one or more        blood metrics measurement apparatus' using the empirical blood        pressure model.    -   User Account Identifier(s): Identifier(s) that identify one or        more user account(s) associated with the blood metrics        measurement apparatus that use the empirical blood pressure        model.

The blood pressure results records 1436 may include one or morecalculated arterial blood pressure values. In some embodiments, theblood pressure results records 1436 may store some or all of thefollowing information:

-   -   Blood Pressure Result Identifier: Identifies a set of one or        more calculated arterial blood pressure values.    -   Blood Pressure Values: one or more calculated arterial blood        pressure values.    -   Date: A date and/or time the arterial blood pressure was        calculated.    -   Messages: Message identifier and/or messages generated based on        the calculated blood pressure values.    -   Signal Identifier: Identifier that identifies an associated        signal.    -   Wave Identifier(s): Identifiers for the subsets of waves of the        associated signal.    -   Feature Vectors Identifier(s): Identifiers for the one or more        sets of feature vectors used to calculate the arterial blood        pressure values.    -   Apparatus Identifier: Identifier that identifies the blood        metrics measurement apparatus that generated the signals.    -   User Account Identifier: Identifier that identifies a user        account associated with the blood metrics measurement apparatus        that generated the signals.

Wave Selection Rules 1438

The wave selection rules 1438 define attributes and/or functions forselecting (or, “extracting”) high quality waves from a set of waves of asignal. The high quality waves may form a subset of waves.

As shown in FIG. 21, PPG measurements are sensitive to motion andambient light distortions and noise-removal filters may not be effectivewhen there is intense noise. Therefore, it may be helpful to select highquality PPG waves from the measured time-series data. The wave selectionrules 1438 based on identifying the wave valleys whose frequency matcheswith the heart rate of the user and then checking how close the wave isto a bi-Gaussian model. An example bi-Gaussian mixture model 2700 isshown in FIG. 27.

The wave selection module 1418 may be configured to execute the waveselection rules 1438. Thus, for example, the wave selection module 1418,using some or all of the associated waves and/or values stored in thesignal records 1426, may identify one or more subsets of “high quality”waves. The wave database 1406 may be configured to store the subsets ofwaves identified by the wave selection module 1418. An example result ofexecution of the wave selection rules 1438 is shown in FIG. 22.

Feature Extraction Rules 1440

The feature extraction rules 1438 define attributes and/or functions foridentifying (or, “extracting”) features from the waves of the selectedsubsets of waves of a signal.

In some embodiments, once the waves are extracted, they may be useddirectly with deep learning algorithms so that both the model and thefeatures may be learned from the PPG waves or pre-determined features ofthe waves may be extracted to use in training of traditional machinelearning algorithms. Examples features that may be used in calculatingarterial blood pressure are described below, although it will beappreciated that like the other examples in this paper, these arenon-limiting examples.

In some embodiments, the features may include time distances within awave to its main peak at different amplitude locations. One example isshown in FIG. 25A for 0% amplitude location. In FIG. 25A, d1 and d2represent the distance between the rising (falling) edge value of thewave and its main (systolic) peak. The secondary (diastolic) peak may beextracted from the first order derivative (FOD) of the wave. For this,FOD may be smoothed (e.g., a simple moving average filter may be used).In some embodiments, after extracting main (systolic) and secondary(diastolic) peaks, reflection (augmentation) index (the ratio ofdiastolic peak and systolic peak amplitudes), inflection point arearatio (the ratio of areas under the wave that are separated by thediastolic inflection point), and/or stiffness index (the ratio ofpatient's height to the time distance between the systolic and diastolicpeaks) may be determined from the first order derivative of the wave.

In the example of FIG. 25C, the second order derivative of the wave hasmultiple peaks and valley points (e.g., labeled (a), (b) and (e)). Insome embodiments additional peak and valleys may be present. If thesampling frequency is low or under exercise conditions (e.g., about 25Hz in FIG. 25C), only waves (a), (b) and (e) may be identifiable. Insome embodiments, for higher sampling frequencies (e.g., ≧200 Hz),additional peaks and valleys may be identifiable. The ratio of thesevalues may be included into a feature vector.

As indicated, an example selected (or, extracted) wave, first orderderivative of the selected wave, and the second derivative of theselected wave are shown in FIGS. 25A-C, respectively.

In some embodiments, if two LEDs at the same wavelength are positionedat different locations of the same artery, a phase shift may be obtainedbetween the measured PPG signals, e.g., due to blood flow. This mayfacilitate calculation of pulse wave velocity (PWV) and/or pulse transittime (PTT), both of which may be included in a feature vector, and/orotherwise provided to the empirical blood pressure model used tocalculate arterial blood pressure values.

Additionally, in some embodiments, a user's gender, age, skin color,height and/or weight may comprise features and may be included in afeature vector, and/or otherwise provided to the empirical bloodpressure model used to calculate arterial blood pressure values.

The feature extraction module 1420 may be configured to execute thefeature extraction rules 1440. Thus, for example, the feature extractionmodule 1418, using some or all of the associated subsets of waves and/orvalues stored in the wave records 1428, may identify one or more featureof the waves within the subsets of waves, and generate correspondingsets of feature vectors. An example of a feature vector 2400 is shown inFIG. 24. The wave feature database 1408 may be configured to store thewave features identified by the feature extraction module 1420, and thefeature vector database 1410 may be configured to store the generatedsets of feature vectors.

Blood Pressure Processing Rules 1442

The blood pressure processing rules 1442 define attributes and/orfunctions for calculating arterial blood pressure values of a user. Insome embodiments, the blood pressure processing rules 1442 specify,identify, and/or define the empirical blood pressure model to use forcalculating arterial blood pressure of a user. The rules 1442 mayfurther define input values for the empirical blood pressure model. Forexample, input values may comprises the sets of features vectors, and/orother attributes of the user (e.g., age, gender, height, weight, skincolor, etc.), assuming such attributes have not been included in thefeature vectors.

The blood pressure processing module 1422 may be configured to executethe blood pressure processing rules 1442. Thus, for example, the bloodpressure processing module 1422, using an empirical blood pressure modelstored in the blood pressure model database 1412, along with some or allof the associated sets of feature vectors and/or values stored in thefeature vector records 1438, may calculate one or more arterial bloodpressure values. The blood pressure results database 1414 may beconfigured to store the blood pressure values calculated by the bloodpressure processing module 1422.

Message Rules 1444

The message rules 1444 define attributes and/or functions for generatingmessages and/or alerts based on arterial blood pressure values. In someembodiments, the message rules 1444 may define rules that cause theblood pressure calculation system 1206 to provide calculate bloodpressure values to a user. In some embodiments, the message rules 1444may include threshold values and/or conditions that when exceeded and/orsatisfied, trigger a message or alert. For example, a threshold value(or value range) and/or threshold condition may be associated withvarying blood pressure levels (e.g., hypotension, normal blood pressure,prehypertension, stage 1 hypertension, stage 2 hypertension, etc.), anda calculated blood pressure value which satisfies a correspondingthreshold condition or value may trigger a message or alert (e.g.,indicating the corresponding blood pressure level).

In some embodiments, the communication module 1424 may be configured toexecute the message rules 1444. Thus, for example, the communicationmodule 1418, using some or all of the blood pressure values stored inthe blood pressure results records 1436, may generate one or moremessages. The communication module 1424 may be configured to providethose messages to a user.

In some embodiments, the communication module 1424 may be configured tosend requests to and receive data from one or a plurality of systems.The communication module 1424 may send requests to and receive data froma systems through a network or a portion of a network. Depending uponimplementation-specific or other considerations, the communicationmodule 1424 may send requests and receive data through a connection(e.g., the communication network 908, and/or the communication link910), all or a portion of which may be a wireless connection. Thecommunication module 1424 may request and receive messages, and/or othercommunications from associated systems.

FIG. 15 shows a flowchart 1500 of an example method of operation of ablood pressure calculation system (e.g., blood pressure calculationsystem 1206) according to some embodiments.

In step 1502, the blood pressure calculation system stores one or moreempirical blood pressure models. For example, the one or more empiricalblood pressure models may be received from a blood metrics server (e.g.,blood metrics server 906), and may comprise specific or non-specificempirical blood pressure models. In some embodiments, a managementmodule (e.g., management module 1402) stores the one or more empiricalblood pressure models in a blood pressure model database (e.g., bloodpressure model database 1414), and/or a communication module (e.g.,communication module 1424 receives the one or more empirical bloodpressure models.

In step 1504, the blood pressure calculation system receives a firstsignal (e.g., a single-channel or multi-channel PPG signal) and a secondsignal (e.g., a single-channel or multi-channel PPG signal). Forexample, the first signal may comprise a 7-channel PPG signal generatedfrom light energy emitted at seven different wavelengths (e.g., 523 nm,590 nm, 623 nm, 660 nm, 740 nm, 850 nm, 940 nm) from one or more lightsources (e.g., seven different LEDs), and the second signal may comprisea PPG signal generated from light energy generated from an additionallight source (e.g., LED) at the same, or substantially similar,wavelength as one the wavelengths of the first signal. Usingmulti-channel signals and/or different light sources may help ensure,for example, that good quality signals may be obtained in a variety ofcircumstances, e.g., a user moving, walking, running, sleeping, and soforth. In some embodiments, a communication module (e.g., communicationmodule 1424 and/or communication module 1208) receives the first andsecond signals from a blood metrics measurement apparatus (e.g., bloodmetrics measurement apparatus 902).

In steps 1506-1508, the blood pressure calculation system identifies afirst subset of waves (or, “high quality” waves) from a first set ofwaves of the first signal and a second subset of waves (or, “highquality” waves) from a second set of waves of the second signal. Each ofthe first subset of waves may represent a separate approximation of anaverage of the first set of waves over a first predetermined amount oftime (e.g., 8 seconds). Similarly, each of the second subset of wavesmay represent a separate approximation of an average of the second setof waves over a second predetermined amount of time (e.g., 8 seconds).In some embodiments, the first and second predetermined amounts of timemay be the same or they may be different. In some embodiments, a waveselection module (e.g., wave selection module 1418) identifies thesubsets of waves.

In steps 1510-1512, the blood pressure calculation system generates afirst set of feature vectors and a second set of feature vectors. Thefirst set of feature vectors may be generated from the first subset ofwaves, and the second set of feature vectors may be generated from thesecond subset of waves. Each of the feature vectors may includemeasurement values and/or metric values. For example, the measurementvalues may correspond to amplitude (e.g., peak-to-peak amplitude, peakamplitude, semi-amplitude, root mean square amplitude, pulse amplitude,etc.) and/or location points of a particular wave (e.g., a correspondinghigh quality wave), and the metric values may be generated from metricfunctions that use at least one of the measurement values. In someembodiments, a feature extraction module (e.g., feature extractionmodule 1420) generates the sets of feature vectors.

In some embodiments, measurement values may include wave peak locationsand/or amplitudes, wave valley locations and/or amplitudes, a wave'sfirst or higher order derivative peak locations and/or amplitudes, awave's first or higher order derivative valley locations and/oramplitudes, and/or or first or higher order moments of a wave. Invarious embodiments, the metric functions may include one or moreparticular metric functions that calculate a distance between aplurality of measurement values. For example, a metric function maycalculate a distance between location points of a particular wave (e.g.,a particular wave peak and a particular wave valley).

In step 1514, the blood pressure calculation system may select anempirical blood pressure model. For example, the blood pressurecalculation system may select an empirical blood pressure model from theone or more empirical blood pressure models stored in the blood pressuremodel database. In some embodiments, a blood pressure processing module(e.g., blood pressure processing module 1422) selects the empiricalblood pressure model.

In some embodiments, the blood pressure calculation system may operatein different modes. For example, in a first mode of operation, the bloodpressure calculation system may utilize a non-specific type of empiricalblood pressure model which does not require further calibration in orderto be used to calculation arterial blood pressure values. Accordingly,in such a first mode of operation, selecting the empirical bloodpressure model may comprise selecting the non-specific empirical bloodpressure model from the empirical blood pressure models stored in theblood pressure database. To continue the example, in a second mode ofoperation, the blood pressure calculation system may utilize a specifictype of empirical blood pressure model which requires at least somecalibration prior to being used to calculate arterial blood pressurevalues. In such a second mode of operation, selecting an empirical bloodpressure model may comprise selecting an empirical blood pressure modelfrom the one or more blood pressure models stored in the blood pressurebased on one or more attributes of the user (e.g., gender, weight,height, skin color, and/or age) and/or other parameters. In someembodiments, a blood pressure processing module (e.g., blood pressureprocessing module 1422) selects the empirical blood pressure model.

In step 1516, the blood pressure calculation system calculates one ormore arterial blood pressure values based on the first set of featurevectors, the second set of feature vectors, and an empirical bloodpressure calculation model (e.g., the model selected in step 1514). Forexample, the empirical blood pressure calculation model may beconfigured to receive the first set of feature vectors and the secondset of feature vectors as input values. In some embodiments, the bloodpressure processing module calculates the one or more arterial bloodpressure values.

In step 1518, the blood pressure calculation system may generate and/orprovide a message including or being based on the arterial bloodpressure. In some embodiments, the communication module generates and/orprovides the message to a user.

FIG. 16 shows a flowchart 1600 of an example method of operation of ablood pressure calculation system (e.g., blood pressure calculationsystem 1206) according to some embodiments. For example, some or all ofthe following steps may be applied to a segment (e.g., an 8 secondsegment) of each channel of a multi-channel signal (e.g., multi-channelPPG signal) to identify subsets of high quality waves of themulti-channel signal.

In step 1602, the blood pressure calculation system identifies peakvalues smaller (or, less) than a mean subtracted by a standard deviationof the signal (e.g., PPG signal). In some embodiments, a wave selectionmodule (e.g., wave selection module 1418) performs the identification.

In step 1604, the blood pressure calculation system selects peak valleyswhose distances with a next peak valley are within a threshold value(e.g., 5%) of a heart rate value and whose intensities are within athreshold value (e.g., 5%) of each other. In some embodiments, the waveselection module performs the selection.

In step 1606, the blood pressure calculation system determines whetherthere is only one identifiable peak between the detected valleys, whoseintensity value is greater than a mean plus one standard deviation. Insome embodiments, the wave selection module performs the determination.

In step 1608, the blood pressure calculation system determines whetherthe selected wave after normalization (e.g., 0-1) satisfies abi-Gaussian mixture model and the model mismatch error is within apredetermined (e.g., user-defined) threshold value (e.g., 2%). In someembodiments, the wave selection module performs the determination. FIG.27 shows an example bi-Gaussian mixture model 2700 for a PPG signal.

In step 1610, the blood pressure calculation system determines whetherthe fitted model has a reflection coefficient between minimum andmaximum possible values (e.g., 0.3-0.7), where the reflectioncoefficient is defined as the ratio of the diastolic peak to thesystolic peak values. In some embodiments, the wave selection moduleperforms the determination.

In step 1612, the blood pressure calculation system selects the wavessatisfying all conditions from steps 1606-1610. The selected waves maybe grouped into subsets of “high quality” waves. In some embodiments,the selected waves may be stored in a training dataset with theassociated channel number. In some embodiments, the wave selectionmodule performs the selection and/or storage.

FIG. 17 depicts a block diagram 1700 of a blood metrics server 906according to some embodiments. Generally, the blood metrics server 906may be configured to generate and/or store empirical blood pressuremodels, provide empirical blood pressure models to a blood pressurecalculation system, register user account and blood metrics measurementapparatus′, and communicate with other systems of the system andenvironment 900. In some embodiments, the blood metrics server 906includes a management module 1702, a registration database 1704, a bloodpressure database 1706, a model selection module 1708, and acommunication module 1710.

The management module 1702 may be configured to manage (e.g., create,read, update, delete, or access) user account records 1714 and bloodmetrics measurement apparatus records 1716 stored in the registrationdatabase 1704, and blood pressure model records 1718 stored in the bloodpressure model database 1706. The management module 1402 may performthese operations manually (e.g., by an administrator interacting with aGUI) and/or automatically (e.g., by one or more of the modules 1708 or1710). In some embodiments, the management module 1702 comprises alibrary of executable instructions which are executable by a processorfor performing any of the aforementioned management operations. Thedatabases 1704 and 1706 may be any structure and/or structures suitablefor storing the records 1714-1718 (e.g., an active database, arelational database, a table, a matrix, an array, a flat file, and thelike).

The user account records 1714 may include a variety of informationassociated with users, user accounts, associated blood metricsmeasurement apparatus' and/or associated user devices. For example, theuser account records 1714 may store some or all of the followinginformation:

-   -   User Account Identifier: Identifier that identifies a user        account.    -   Password: Password, or other personal identifier, used to        authenticate the user account. For example, it may an        alphanumerical password, biometric data (e.g., fingerprint,        etc.). In some embodiments, readings or measurements from the        blood metrics measurement apparatus 902 may be used to        authenticate the user account.    -   Device Identifier(s): Identifier(s) that identify one or more        blood metrics measurement apparatus' associated with the user        account and/or user device.    -   Name: A name of the user.    -   DOB: A date of birth of the user.    -   Age: An age of the user.    -   Gender: Gender of the user, e.g., female, male, transgender,        etc.    -   Weight: A weight of the user.    -   Height: A height of the user.    -   Skin color: A skin color of the user.    -   Activity Level: An activity level of the user, e.g., sedentary,        lightly active, active, very active, and so forth.    -   Geographic location: A location of the user, e.g., as determined        by a location service and/or specified by the user.    -   Blood Pressure Profile: Hypertensive, Hypotensive, Normal or        unknown.    -   Blood Glucose Profile (e.g., Diabetes information)    -   Wrist circumference: Circumference of the user's wrist.

The blood metrics measurement apparatus registration records 1716 mayinclude a variety of information associated with users, user accounts,associated blood metrics measurement apparatus' and/or associated userdevices. For example, the blood metrics measurement apparatusregistration records 1716 may store some or all of the followinginformation:

-   -   Apparatus Identifier: Identifier that identifies a blood metrics        measurement apparatus.    -   User Account Identifier: Identifier that identifies a user        account and/or user device associated with the blood metrics        measurement apparatus.    -   Geographic location: A current location of the blood metrics        measurement apparatus, e.g., as determined by a location service        and/or specified by the user.    -   Settings: One or more settings of the blood measurement metrics        apparatus. For example, some or all of the settings may be        automatically determined based on one or more user account        attributes (e.g., height, weight, etc.) and/or by the user.

The blood pressure model database 1706 may include one or more empiricalblood pressure model records 1718. The models may include various typesof empirical blood pressure models. For example, a first type may be anon-specific model which does not require calibration in order to beused to calculate arterial blood pressure values. A second type may be aspecific model which requires calibration in order to be used tocalculate arterial blood pressure values. For example, models of thesecond type may require information about the user, such age, weight,height, gender, skin color, and/or the like. In some embodiments, theblood pressure records 1718 may store some or all of the followinginformation:

-   -   Model Identifier: Identifies an empirical blood pressure model.    -   Model Type: Identifies a type of model, e.g., non-specific or        specific.    -   Model Parameters: Various model parameters (e.g., decision node        parameters) and tree structures used to calculate the arterial        blood pressure values based on the sets of feature vectors        and/or other related information (e.g., gender, age, weight,        height, skin color, etc.). Example tree structures are shown in        FIG. 26.    -   Apparatus Identifier(s): Identifier(s) that identify one or more        blood metrics measurement apparatus' using the empirical blood        pressure model.    -   User Account Identifier(s): Identifier(s) that identify one or        more user account(s) associated with the blood metrics        measurement apparatus that use the empirical blood pressure        model.

The model selection module 1708 may be configured to generate empiricalblood pressure models and/or identify an empirical blood pressure modelsstored in the records 1718 to provide to a blood pressure calculationsystem. For example, an empirical blood pressure model may be identifiedin response to a request from a user device or a blood pressurecalculation system. In various embodiments, the model selection module1708 identifies a non-specific empirical blood pressure model which doesnot require further calibration prior to being used by a blood pressurecalculation system to calculate arterial blood pressure values. In someembodiments, the model selection module 1708 includes some or all of thefunctionality of a wave selection module, feature extraction module,blood pressure processing module, along with associated features and/orfunctionality (e.g., a signal database, wave database, and so forth).

In one example, an empirical blood pressure model may be identified froma set of models (e.g., stored in the records 1718) by the modelselection module 1708 based upon a modified Random Forests algorithmusing random decision trees in a regression mode. For example, decisiontrees may comprise a plurality of nodes for decision making. Twoexamples of decision trees are shown in FIG. 26. Each internal node maycorrespond to one comparison point of the input values (e.g., onefeature (F_(i), i=1, . . . , N) a feature vector) and edges mayrepresent the path of the outcomes. Leaves (the end points) mayrepresent desired target points. In this example, target points areequivalent to particular blood pressure values.

In some embodiments, a specified (or, predetermined) number of decisiontrees are constructed. Each decision tree may result in a blood pressuretarget, but majority voting may give a single blood pressure estimate(or, calculation). In a model training (or, “calibration”) phase, thedecision node parameters (a₁, . . . , a_(N), b₁, . . . , b_(N), . . . ),combination of features, and the structure of the decision trees may belearned, for example, by randomly subsampling the training data and thefeatures, and tracking the error in an unobserved part of the trainingdata. In some embodiments, these parameters and tree structures maycomprise the empirical blood pressure models generated and/or stored theblood metrics server 906, and/or used by the blood pressure calculationsystem 1206 to calculate arterial blood pressure values.

In some embodiments, grown decision trees may learn highly complexpatterns. However, they may be prone to overfitting (e.g., sensitive tonoise). The model selection module 1708 may address this problem. Forexample, a tree bagging (or, bootstrap aggregating) technique, in whichsamples are randomly selected with replacement or without replacementfrom a training list. The assignment of unseen samples may be performedby taking a majority vote. Although one decision tree may be sensitiveto noise, using multiple decision trees and averaging results maysignificantly decrease the variance as long as the trees are notcorrelated. In some embodiments, the model selection module 1708combines tree bagging with feature bagging where the subset of featuresare also selected randomly with replacement. In some embodiments, themodel selection module 1708 may randomly subsample the training datasetwithout replacement. In some embodiments, the model selection module1708 may include several parameters to be tuned for a minimum number ofleaves and/or number of trees.

In some embodiments, during a model calibration phase, the datasetscoming from different subjects may be randomly partitioned to trainingsets and test sets. In some embodiments, generally, 70-80% of thedatasets may be used in training, and the datasets of the same subjectmay not be both training and testing sets, i.e., test sets and trainingsets should be disjointed. If the size of the data (number of patientsor users) is limited (e.g., ≦150), a bucketwise randomization may beapplied, e.g., to help sure that there is enough training data forcertain ranges of blood pressure. For example, the bucket edges may beselected as 80, 100, 130, 160, 180, 230. Then, for example, randompartitioning may be separately applied to data sets within a certainbucket so that the ratio of training and test samples will be the same.

In some embodiments, the parameters of the tree bagging technique areoptimized using k-fold cross validation. Generally, in k-fold crossvalidation, the existing dataset may be randomly partitioned intotraining and testing k-times (e.g., k≧1). This may be done, for example,to decrease the bias of the results to the selected test and trainingsets. Error metrics between the ground truth and estimated bloodpressure values in test dataset are averaged k-times. Those metrics mayinclude, and are not limited to, mean square error (MSE, ExampleEquation 1, shown below), root mean square error (RMSE, Example Equation2, shown below), median absolute deviation (MAD, Example Equation 3,shown below), and/or coefficient of determination (R², Example Equation4, shown below). In some embodiments, the model giving optimal (or, thelowest) error values may be selected and used by the blood pressurecalculation system 1206 to calculate arterial blood pressure values.

$\begin{matrix}{{{MSE}\left( \hat{y} \right)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\;\left( {y_{i} - {\hat{y}}_{i}} \right)^{2}}}} & {{Example}\mspace{14mu}{Equation}\mspace{14mu} 1} \\{{{RMSE}\left( \hat{y} \right)} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\;\left( {y_{i} - {\hat{y}}_{i}} \right)^{2}}}} & {{Example}\mspace{14mu}{Equation}\mspace{14mu} 2} \\{{{MAD}\left( \hat{y} \right)} = {{median}\left( {{y - \hat{y}}} \right)}} & {{Example}\mspace{14mu}{Equation}\mspace{14mu} 3} \\{{R^{2}\left( \hat{y} \right)} = {1 - \frac{{MSE}\left( \hat{y} \right)}{{var}(y)}}} & {{Example}\mspace{14mu}{Equation}\mspace{14mu} 4}\end{matrix}$

In some embodiments, the communication module 1710 may be configured tosend requests to and receive data from one or a plurality of systems.The communication module 1710 may send requests to and receive data froma systems through a network or a portion of a network. Depending uponimplementation-specific or other considerations, the communicationmodule 1710 may send requests and receive data through a connection(e.g., the communication network 908, and/or the communication link910), all or a portion of which may be a wireless connection. Thecommunication module 1710 may request and receive messages, and/or othercommunications from associated systems.

FIG. 18 shows a flowchart 1800 of an example method of operation of ablood metrics server (e.g., blood metrics server 906) according to someembodiments.

In step 1802, the blood metrics server receives and processes useraccount and/or blood pressure measurement apparatus registrationrequests. In some embodiments a communication module (e.g.,communication module 1712) receives the requests. An exampleregistration method is shown in FIG. 20.

In step 1804, the blood metrics server receives and processes a requestfor an empirical blood pressure model. In some embodiments, thecommunication module receives the request from a user device (e.g., userdevice 904) and/or blood pressure calculation system (e.g., bloodpressure calculation system 1206). For example, the user device and/orblood pressure calculation system may periodically request an updatedmodel.

In step 1806, the blood metrics server selects an empirical bloodpressure model. For example, the model may be selected in response tothe request, although in some embodiments, the blood metrics server mayautomatically select a model in order to push down an updated model tothe user device. In some embodiments, a model selection module (e.g.,model selection 1708) performs the selection.

In step 1808, the blood metrics server provides the selected empiricalblood pressure to the user device and/or blood pressure calculationsystem. In some embodiments, the communication module provides theselected empirical blood pressure model.

FIG. 19 shows a flowchart 1900 of an example method of operation of ablood metrics server (e.g., blood metrics server 906) according to someembodiments.

In step 1902, the blood metrics server receives a first signal (e.g., asingle-channel or multi-channel PPG signal) and a second signal (e.g., asingle-channel or multi-channel PPG signal) for each of a plurality ofusers (e.g., training subjects). For example, the first signal maycomprise a 7-channel PPG signal generated from light energy emitted atseven different wavelengths (e.g., 523 nm, 590 nm, 623 nm, 660 nm, 740nm, 850 nm, 940 nm) from one or more light sources (e.g., sevendifferent LEDs), and the second signal may comprise a single-channel PPGsignal generated from light energy generated from an additional lightsource (e.g., LED) at the same, or substantially similar, wavelength asone the wavelengths of the first signal. Using multi-channel signalsand/or different light sources may help ensure, for example, that goodquality signals may be obtained in a variety of circumstances, e.g., auser moving, walking, running, sleeping, and so forth. In someembodiments, a communication module (e.g., communication module 1712receives the first and second signals from a blood metrics measurementapparatus (e.g., blood metrics measurement apparatus 902) and/or otherblood pressure measurement devices, such as other types of non-invasivedevices (e.g., sphygmomanometer) and/or invasive devices (e.g.,catheters, tubes, etc.).

In step 1904-1906, the blood metrics server identifies, for each of theplurality of users, a first subset of waves (or, “high quality” waves)from a first set of waves of the first signal and a second subset ofwaves (or, “high quality” waves) from a second set of waves of thesecond signal, each of the first subset of waves representing a separateapproximation of an average of the first set of waves over apredetermined amount of time and each of the second subset of wavesrepresenting a separate approximation of an average of the second set ofwaves over the predetermined amount of time. In some embodiments, amodel selection module (e.g., model selection module 1708) identifiesthe subsets of waves.

In step 1908-1910, the blood metrics server generates, for each of theplurality of users, a first set of feature vectors and a second set offeature vectors. The first set of feature vectors may be generated fromthe first subset of waves, and the second set of feature vectors may begenerated from the second subset of waves. Each of the feature vectorsmay include measurement values and metric values. For example, themeasurement values may correspond to amplitude (e.g., peak-to-peakamplitude, peak amplitude, semi-amplitude, root mean square amplitude,pulse amplitude, etc.) or location points of a particular wave (e.g., acorresponding high quality wave), and the metric values may be generatedfrom metric functions that use at least one of the measurement values.In some embodiments, the model selection module generates the sets offeature vectors.

In step 1912, the blood metrics server generates a plurality ofempirical blood pressure models based on the feature vectors. In someembodiments, the model selection module generates the models.

In step 1914, the blood metrics server identifies a particular one ofthe plurality of empirical blood pressure models. The identified modelmay be used to calculate arterial blood pressure values withoutrequiring further calibration (e.g., a non-specific type of empiricalblood pressure model). In some embodiments, the model selection moduleperforms the identification.

In step 1916, the blood metrics server stores the particular one of theplurality of empirical blood pressure models. In some embodiments, amanagement module (e.g., management module 1702) stores the particularmodel in a database (e.g., blood pressure model database 1706).

FIG. 20 shows a flowchart 2000 of an example method of operation of ablood metrics server (e.g., blood metrics server 906) according to someembodiments.

In step 2002, the blood metrics server receives a user accountregistration request from a user device (e.g., user device 904) via acommunication network (e.g., communication network 908). The useraccount registration request may include, for example, a username (e.g.,“jsmith2015), password, full name (e.g., “John Smith”), birthdate,gender, physical characteristics (e.g., height, weight, etc.), and soforth. In some embodiments a communication module (e.g., communicationmodule 1716) receives the user account registration request.

In step 2004, if the blood metrics server approves the registrationrequest, the blood metrics server may create a new user accountregistration record (e.g., user account registration record 1714) in aregistration database (e.g., registration database 1704) and/or anaccount for the user. In some embodiments, a management module (e.g.,management module 1702) creates the user account registration record.

In step 2006, the blood metrics server receives a log-in request fromthe user device. The log-in request may include, for example, theusername and password. If the log-in credentials are correct, the bloodmetrics server logs the user account into the user device. In someembodiments the communication module receives the log-in request.

In step 2008, the blood metrics server receives a blood metricsmeasurement apparatus registration request from the user device. Theblood metrics measurement apparatus registration request may include,for example, an apparatus identifier that identifies the blood metricsmeasurement apparatus, and a user account identifier associated the useraccount and/or user device. The apparatus identifier may be obtained bythe user device by a variety of methods, e.g., scanning a physicalfeature (e.g., a tag, code, etc.) of the blood metrics measurementapparatus, entering the identifier manually via the user device, and/orthe like. In some embodiments the communication module receives theblood metrics measurement apparatus registration request.

In step 2010, the blood metrics server updates the registration databasewith the blood metrics measurement apparatus registration request byeither creating a new record (e.g., a record 1716) or, if the apparatusidentifier is already stored in one of the records (e.g., records 1716),updating that particular record. In some embodiments, the managementmodule updates the registration database.

In step 2012, the blood metrics server provides a registration successmessage to the user device when the registration database has beenupdated. In some embodiments the communication module provides themessage.

FIG. 21 shows an example noisy PPG signal 2100 and an example filteredPPG signal 2150 according to some embodiments.

FIG. 22 shows an example set of waves 2200 of a PPG signal and anexample high quality wave 2202 selected from the set of waves 2200according to some embodiments. FIG. 22 further shows an examplebi-Gaussian fitted signal 2204 according to some embodiments.

FIG. 23 shows example feature points 2302 a-u of a wave 2300 accordingto some embodiments. For example, the feature points 2302 a-u maycomprise location points and/or amplitudes. In some embodiments, some orall of the features points 2302 a-u may correspond to measurement valueswhich may be used by one or more metric functions.

FIGS. 25A-C show an example selected high quality wave 2500, the firstderivative 2502 of the selected high quality wave 2500, and the secondderivative 2504 of the selected high quality wave 2500 according to someembodiments.

FIG. 26 shows example tree structures 2602 and 2604 of an exampleempirical blood pressure calculation model 2600 according to someembodiments.

It will be appreciated that a “user device,” “server,” “module,”“system,” and/or “database” may comprise software, hardware, firmware,and/or circuitry. In one example, one or more software programscomprising instructions capable of being executable by a processor mayperform one or more of the functions of the user devices, servers,modules, systems, and/or databases described herein. In another example,circuitry may perform the same or similar functions. Alternativeembodiments may comprise more, less, or functionally equivalent userdevices, servers, modules, systems, and/or databases, and still bewithin the scope of present embodiments. For example, the blood metricsmeasurement apparatus 902 may include some or all of the functionalityof the user device 904 (e.g., user interface module 1202, blood pressurecalculation system 1206, etc.), the blood metrics server 906 may includesome or all of the functionality of the blood pressure calculationsystem 1206, and so forth.

The present invention(s) are described above with reference to exampleembodiments. It will be apparent to those skilled in the art thatvarious modifications may be made and other embodiments can be usedwithout departing from the broader scope of the present invention(s).Therefore, these and other variations upon the example embodiments areintended to be covered by the present invention(s).

The invention claimed is:
 1. A system comprising: a wearable memberincluding: an energy transmitter configured to project energy at a firstwavelength and energy at a second wavelength into tissue of a user; andan energy receiver configured to generate a first signal based on afirst received portion of the energy at the first wavelength and asecond signal based on a second received portion of the energy at thesecond wavelength, the first received portion of the energy and thesecond received portion of the energy each being received through thetissue of the user; and a digital device including: memory; and aprocessor configured to: identify a first subset of waves from a firstset of waves of the first signal and a second subset of waves from asecond set of waves of the second signal, each of the first subset ofwaves representing a separate average of the first set of waves over apredetermined amount of time and each of the second subset of wavesrepresenting a separate average of the second set of waves over thepredetermined amount of time, generate a first set of feature vectorsand a second set of feature vectors, the first set of feature vectorsgenerated from the first subset of waves, the second set of featurevectors generated from the second subset of waves, wherein each of thefeature vectors of the first set of feature vectors and the second setof feature vectors include measurement values and metric values, themeasurement values corresponding to amplitude or location points of aparticular wave, the metric values generated from metric functions thatuse at least one of the measurement values, calculate an arterial bloodpressure value based on the first set of feature vectors, the second setof feature vectors, and an empirical blood pressure calculation model,the empirical blood pressure calculation model configured to receive thefirst set of feature vectors and the second set of feature vectors asinput values, and provide a message including or being based on thearterial blood pressure value.
 2. The system of claim 1, wherein theenergy transmitter includes a first light source and a second lightsource, the first light source configured to project the energy at thefirst wavelength, the second light source configured to project theenergy at the second wavelength.
 3. The system of claim 2, wherein thefirst light source and the second light source each comprise alight-emitting diode (LED).
 4. The system of claim 1, wherein themeasurement values include any of wave peak locations or amplitudes, orwave valley locations or amplitudes.
 5. The system of claim 1, whereinthe measurement values include any of an associated wave's first orhigher order derivative peak locations or amplitudes, the associatedwave's first or higher order derivative valley locations or amplitudes,or first or higher order moments of the associated wave.
 6. The systemof claim 1, wherein the metric functions include one or more particularmetric functions that calculate a distance between two measurementvalues.
 7. The system of claim 2, wherein the energy projected by thefirst light source and the energy projected by the second light sourceeach have the same wavelength.
 8. The system of claim 7, wherein theprocessor is further configured to: determine a phase shift between thefirst signal and the second signal; calculate, based on the phase shift,any of a pulse wave velocity or a pulse transit time; calculate thearterial blood pressure value based on the first set of feature vectors,the second set of feature vectors, any of the pulse wave velocity or thepulse transit time, and the empirical blood pressure calculation model;and receive the first set of feature vectors, the second set of featurevectors, and any of the pulse wave velocity or the pulse transit time asinput.
 9. The system of claim 2, wherein the first light source isfurther configured to project one or more wavelengths in addition to thefirst wavelength, and wherein the second wavelength is the same, orsubstantially similar to, one of the wavelengths projected from thefirst light source.
 10. The system of claim 1, wherein the first signaland the second signal each comprise a photoplethysmogram (PPG) signal.11. A method comprising: projecting, at an energy transmitter, energy ata first wavelength and energy at a second wavelength into tissue of auser; generating, at the energy transmitter, a first signal based on afirst received portion of the energy at the first wavelength and asecond signal based on a second received portion of the energy at thesecond wavelength, the first received portion of the energy and thesecond received portion of the energy each being received through thetissue of the user; identifying, at a blood pressure calculation system,a first subset of waves from a first set of waves of the first signaland a second subset of waves from a second set of waves of the secondsignal, each of the first subset of waves representing a separateaverage of the first set of waves over a predetermined amount of timeand each of the second subset of waves representing a separate averageof the second set of waves over the predetermined amount of time;generating, at the blood pressure calculation system, a first set offeature vectors and a second set of feature vectors, the first set offeature vectors generated from the first subset of waves, the second setof feature vectors generated from the second subset of waves, whereineach of the feature vectors of the first set of feature vectors and thesecond set of feature vectors include measurement values and metricvalues, the measurement values corresponding to amplitude or locationpoints of a particular wave, the metric values generated from metricfunctions that use at least one of the measurement values; calculating,at the blood pressure calculation system, an arterial blood pressurevalue based on the first set of feature vectors, the second set offeature vectors, and an empirical blood pressure calculation model, theempirical blood pressure calculation model configured to receive thefirst set of feature vectors and the second set of feature vectors asinput values; and providing, from the blood pressure calculation system,a message including or being based on the arterial blood pressure value.12. The method of claim 11, wherein the energy transmitter includes afirst light source and a second light source, the first light sourceconfigured to project the energy at the first wavelength, the secondlight source configured to project the energy at the second wavelength.13. The method of claim 12, wherein the first light source and thesecond light source each comprise a light-emitting diode (LED).
 14. Themethod of claim 11, wherein the measurement values include any of wavepeak locations or amplitudes, or wave valley locations or amplitudes.15. The method of claim 11, wherein the measurement values include anyof an associated wave's first or higher order derivative peak locationsor amplitudes, the associated wave's first or higher order derivativevalley locations or amplitudes, or first or higher order moments of theassociated wave.
 16. The method of claim 11, wherein the metricfunctions include one or more particular metric functions that calculatea distance between two measurement values.
 17. The method of claim 12,wherein the energy projected by the first light source and the energyprojected by the second light source each have the same wavelength. 18.The method of claim 17, further comprising: determining a phase shiftbetween the first signal and the second signal; and calculating, basedon the phase shift, any of a pulse wave velocity or a pulse transittime, wherein the blood pressure calculation module is furtherconfigured to calculate the arterial blood pressure value based on thefirst set of feature vectors, the second set of feature vectors, any ofthe pulse wave velocity or the pulse transit time, and the empiricalblood pressure calculation model, and the empirical blood pressurecalculation model is further configured to receive as the input thefirst set of feature vectors, the second set of feature vectors, and anyof the pulse wave velocity or the pulse transit time.
 19. The method ofclaim 12, wherein the first light source is further configured toproject one or more wavelengths in addition to the first wavelength, andwherein the second wavelength is the same, or substantially similar to,one of the wavelengths projected from the first light source.
 20. Themethod of claim 11, wherein the first signal and the second signal eachcomprise a photoplethysmogram (PPG) signal.
 21. A system comprising: acommunication interface configured to receive a first signal and asecond signal, the first signal being based on a first received portionof energy having been previously projected at a first wavelength intotissue of a user, the second signal being based on a second receivedportion of energy having been previously projected at a secondwavelength into the tissue of the user; memory; and a processorconfigured to: identify a first subset of waves from a first set ofwaves of the first signal and a second subset of waves from a second setof waves of the second signal, each of the first subset of wavesrepresenting a separate average of the first set of waves over apredetermined amount of time and each of the second subset of wavesrepresenting a separate average of the second set of waves over thepredetermined amount of time; generate a first set of feature vectorsand a second set of feature vectors, the first set of feature vectorsgenerated from the first subset of waves, the second set of featurevectors generated from the second subset of waves, wherein each of thefeature vectors of the first set of feature vectors and the second setof feature vectors include measurement values and metric values, themeasurement values corresponding to amplitude or location points of aparticular wave, the metric values generated from metric functions thatuse at least one measurement value; calculate an arterial blood pressurevalue based on the first set of feature vectors, the second set offeature vectors, and an empirical blood pressure calculation model, theempirical blood pressure calculation model configured to receive thefirst set of feature vectors and the second set of feature vectors asinput values; and provide a message including or being based on thearterial blood pressure value.
 22. A system comprising: a processor; andmemory storing instructions that, when executed by the processor, causethe processor to: receive a first signal and a second signal, the firstsignal being based on a first received portion of energy having beenpreviously projected at a first wavelength into tissue of a user, thesecond signal being based on a second received portion of energy havingbeen previously projected at a second wavelength into the tissue of theuser; identify a first subset of waves from a first set of waves of thefirst signal and a second subset of waves from a second set of waves ofthe second signal, each of the first subset of waves representing aseparate average of the first set of waves over a predetermined amountof time and each of the second subset of waves representing a separateaverage of the second set of waves over the predetermined amount oftime; generate a first set of feature vectors and a second set offeature vectors, the first set of feature vectors generated from thefirst subset of waves, the second set of feature vectors generated fromthe second subset of waves, wherein each of the feature vectors of thefirst set of feature vectors and the second set of feature vectorsinclude measurement values and metric values, the measurement valuescorresponding to amplitude or location points of a particular wave, themetric values generated from metric functions that use at least one ofthe measurement values; calculate an arterial blood pressure value basedon the first set of feature vectors, the second set of feature vectors,and an empirical blood pressure calculation model, the empirical bloodpressure calculation model configured to receive the first set offeature vectors and the second set of feature vectors as input values;and provide a message including or being based on the arterial bloodpressure value.